testrun: single card
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wql
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---
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base_model: ../../../models/qwen
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library_name: peft
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license: other
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tags:
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- llama-factory
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- lora
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- generated_from_trainer
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model-index:
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- name: lora_sft_Qwen-7B_1_gpu_50_step_20240918162245
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# lora_sft_Qwen-7B_1_gpu_50_step_20240918162245
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This model is a fine-tuned version of [../../../models/qwen](https://huggingface.co/../../../models/qwen) on the belle_1m dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3353
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- Num Input Tokens Seen: 132048
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- training_steps: 50
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- PEFT 0.12.0
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- Transformers 4.43.4
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- Pytorch 2.1.0
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "../../../models/qwen",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"w2",
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"c_proj",
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"w1",
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"c_attn"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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{
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"epoch": 0.08888888888888889,
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"eval_loss": 1.3352934122085571,
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"eval_runtime": 54.3905,
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"eval_samples_per_second": 18.386,
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"eval_steps_per_second": 9.193,
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"num_input_tokens_seen": 132048,
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"total_flos": 5638623387844608.0,
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"train_loss": 1.4425424814224244,
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"train_runtime": 174.5036,
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"train_samples_per_second": 4.584,
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"train_steps_per_second": 0.287,
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"train_tokens_per_second": 1136.939
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}
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---
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base_model: ../../../models/qwen
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library_name: peft
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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||||
- **Shared by [optional]:** [More Information Needed]
|
||||
- **Model type:** [More Information Needed]
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||||
- **Language(s) (NLP):** [More Information Needed]
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||||
- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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||||
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||||
<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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||||
- **Demo [optional]:** [More Information Needed]
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||||
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## Uses
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||||
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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||||
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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||||
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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||||
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
|
||||
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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||||
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||||
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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||||
- **Hours used:** [More Information Needed]
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||||
- **Cloud Provider:** [More Information Needed]
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||||
- **Compute Region:** [More Information Needed]
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||||
- **Carbon Emitted:** [More Information Needed]
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||||
|
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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||||
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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||||
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## Glossary [optional]
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||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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||||
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[More Information Needed]
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||||
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.12.0
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|
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@ -0,0 +1,31 @@
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "../../../models/qwen",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"w2",
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"c_proj",
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"w1",
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"c_attn"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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{
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|im_end|>"
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}
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# Copyright (c) Alibaba Cloud.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""Tokenization classes for QWen."""
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import base64
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import logging
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import os
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import unicodedata
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from typing import Collection, Dict, List, Set, Tuple, Union
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import tiktoken
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from transformers import PreTrainedTokenizer, AddedToken
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
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PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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ENDOFTEXT = "<|endoftext|>"
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IMSTART = "<|im_start|>"
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IMEND = "<|im_end|>"
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# as the default behavior is changed to allow special tokens in
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# regular texts, the surface forms of special tokens need to be
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# as different as possible to minimize the impact
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EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
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# changed to use actual index to avoid misconfiguration with vocabulary expansion
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SPECIAL_START_ID = 151643
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SPECIAL_TOKENS = tuple(
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enumerate(
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(
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(
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ENDOFTEXT,
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IMSTART,
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IMEND,
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)
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+ EXTRAS
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),
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start=SPECIAL_START_ID,
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)
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)
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SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
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def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
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with open(tiktoken_bpe_file, "rb") as f:
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contents = f.read()
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return {
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base64.b64decode(token): int(rank)
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for token, rank in (line.split() for line in contents.splitlines() if line)
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}
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class QWenTokenizer(PreTrainedTokenizer):
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"""QWen tokenizer."""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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errors="replace",
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extra_vocab_file=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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# how to handle errors in decoding UTF-8 byte sequences
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# use ignore if you are in streaming inference
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self.errors = errors
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self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
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self.special_tokens = {
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token: index
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for index, token in SPECIAL_TOKENS
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}
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# try load extra vocab from file
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if extra_vocab_file is not None:
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used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
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extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
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for token, index in extra_mergeable_ranks.items():
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if token in self.mergeable_ranks:
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logger.info(f"extra token {token} exists, skipping")
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continue
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if index in used_ids:
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logger.info(f'the index {index} for extra token {token} exists, skipping')
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continue
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self.mergeable_ranks[token] = index
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# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
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enc = tiktoken.Encoding(
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"Qwen",
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pat_str=PAT_STR,
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mergeable_ranks=self.mergeable_ranks,
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special_tokens=self.special_tokens,
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)
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assert (
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len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
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), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
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self.decoder = {
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v: k for k, v in self.mergeable_ranks.items()
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} # type: dict[int, bytes|str]
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self.decoder.update({v: k for k, v in self.special_tokens.items()})
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self.tokenizer = enc # type: tiktoken.Encoding
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self.eod_id = self.tokenizer.eot_token
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self.im_start_id = self.special_tokens[IMSTART]
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self.im_end_id = self.special_tokens[IMEND]
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def __getstate__(self):
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# for pickle lovers
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state = self.__dict__.copy()
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del state["tokenizer"]
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return state
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def __setstate__(self, state):
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# tokenizer is not python native; don't pass it; rebuild it
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self.__dict__.update(state)
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enc = tiktoken.Encoding(
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"Qwen",
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pat_str=PAT_STR,
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mergeable_ranks=self.mergeable_ranks,
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special_tokens=self.special_tokens,
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)
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self.tokenizer = enc
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def __len__(self) -> int:
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return self.tokenizer.n_vocab
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def get_vocab(self) -> Dict[bytes, int]:
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return self.mergeable_ranks
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def convert_tokens_to_ids(
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self, tokens: Union[bytes, str, List[Union[bytes, str]]]
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) -> List[int]:
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ids = []
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if isinstance(tokens, (str, bytes)):
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if tokens in self.special_tokens:
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return self.special_tokens[tokens]
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else:
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return self.mergeable_ranks.get(tokens)
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for token in tokens:
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if token in self.special_tokens:
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ids.append(self.special_tokens[token])
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else:
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ids.append(self.mergeable_ranks.get(token))
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return ids
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def _add_tokens(
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self,
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new_tokens: Union[List[str], List[AddedToken]],
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special_tokens: bool = False,
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) -> int:
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if not special_tokens and new_tokens:
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raise ValueError("Adding regular tokens is not supported")
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for token in new_tokens:
|
||||
surface_form = token.content if isinstance(token, AddedToken) else token
|
||||
if surface_form not in SPECIAL_TOKENS_SET:
|
||||
raise ValueError("Adding unknown special tokens is not supported")
|
||||
return 0
|
||||
|
||||
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
||||
"""
|
||||
Save only the vocabulary of the tokenizer (vocabulary).
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
||||
with open(file_path, "w", encoding="utf8") as w:
|
||||
for k, v in self.mergeable_ranks.items():
|
||||
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
||||
w.write(line)
|
||||
return (file_path,)
|
||||
|
||||
def tokenize(
|
||||
self,
|
||||
text: str,
|
||||
allowed_special: Union[Set, str] = "all",
|
||||
disallowed_special: Union[Collection, str] = (),
|
||||
**kwargs,
|
||||
) -> List[Union[bytes, str]]:
|
||||
"""
|
||||
Converts a string in a sequence of tokens.
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
The sequence to be encoded.
|
||||
allowed_special (`Literal["all"]` or `set`):
|
||||
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
||||
Default to "all".
|
||||
disallowed_special (`Literal["all"]` or `Collection`):
|
||||
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
||||
Default to an empty tuple.
|
||||
|
||||
kwargs (additional keyword arguments, *optional*):
|
||||
Will be passed to the underlying model specific encode method.
|
||||
|
||||
Returns:
|
||||
`List[bytes|str]`: The list of tokens.
|
||||
"""
|
||||
tokens = []
|
||||
text = unicodedata.normalize("NFC", text)
|
||||
|
||||
# this implementation takes a detour: text -> token id -> token surface forms
|
||||
for t in self.tokenizer.encode(
|
||||
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
||||
):
|
||||
tokens.append(self.decoder[t])
|
||||
return tokens
|
||||
|
||||
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
||||
"""
|
||||
Converts a sequence of tokens in a single string.
|
||||
"""
|
||||
text = ""
|
||||
temp = b""
|
||||
for t in tokens:
|
||||
if isinstance(t, str):
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
temp = b""
|
||||
text += t
|
||||
elif isinstance(t, bytes):
|
||||
temp += t
|
||||
else:
|
||||
raise TypeError("token should only be of type types or str")
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
return text
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
||||
"""Converts an id to a token, special tokens included"""
|
||||
if index in self.decoder:
|
||||
return self.decoder[index]
|
||||
raise ValueError("unknown ids")
|
||||
|
||||
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
||||
"""Converts a token to an id using the vocab, special tokens included"""
|
||||
if token in self.special_tokens:
|
||||
return self.special_tokens[token]
|
||||
if token in self.mergeable_ranks:
|
||||
return self.mergeable_ranks[token]
|
||||
raise ValueError("unknown token")
|
||||
|
||||
def _tokenize(self, text: str, **kwargs):
|
||||
"""
|
||||
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
||||
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
||||
|
||||
Do NOT take care of added tokens.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def _decode(
|
||||
self,
|
||||
token_ids: Union[int, List[int]],
|
||||
skip_special_tokens: bool = False,
|
||||
errors: str = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
if isinstance(token_ids, int):
|
||||
token_ids = [token_ids]
|
||||
if skip_special_tokens:
|
||||
token_ids = [i for i in token_ids if i < self.eod_id]
|
||||
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
{
|
||||
"added_tokens_decoder": {},
|
||||
"auto_map": {
|
||||
"AutoTokenizer": [
|
||||
"tokenization_qwen.QWenTokenizer",
|
||||
null
|
||||
]
|
||||
},
|
||||
"chat_template": "{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|im_end|>",
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|im_end|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "QWenTokenizer"
|
||||
}
|
||||
|
|
@ -0,0 +1,161 @@
|
|||
{
|
||||
"best_metric": null,
|
||||
"best_model_checkpoint": null,
|
||||
"epoch": 0.08888888888888889,
|
||||
"eval_steps": 500,
|
||||
"global_step": 50,
|
||||
"is_hyper_param_search": false,
|
||||
"is_local_process_zero": true,
|
||||
"is_world_process_zero": true,
|
||||
"log_history": [
|
||||
{
|
||||
"epoch": 0.005333333333333333,
|
||||
"grad_norm": 0.8999722599983215,
|
||||
"learning_rate": 4e-05,
|
||||
"loss": 1.5189,
|
||||
"num_input_tokens_seen": 9808,
|
||||
"step": 3
|
||||
},
|
||||
{
|
||||
"epoch": 0.010666666666666666,
|
||||
"grad_norm": NaN,
|
||||
"learning_rate": 6e-05,
|
||||
"loss": 1.5504,
|
||||
"num_input_tokens_seen": 19312,
|
||||
"step": 6
|
||||
},
|
||||
{
|
||||
"epoch": 0.016,
|
||||
"grad_norm": 0.9268227219581604,
|
||||
"learning_rate": 9.987820251299122e-05,
|
||||
"loss": 1.5661,
|
||||
"num_input_tokens_seen": 29232,
|
||||
"step": 9
|
||||
},
|
||||
{
|
||||
"epoch": 0.021333333333333333,
|
||||
"grad_norm": 1.1588999032974243,
|
||||
"learning_rate": 9.806308479691595e-05,
|
||||
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|
||||
"num_input_tokens_seen": 37984,
|
||||
"step": 12
|
||||
},
|
||||
{
|
||||
"epoch": 0.02666666666666667,
|
||||
"grad_norm": 1.0571134090423584,
|
||||
"learning_rate": 9.567727288213005e-05,
|
||||
"loss": 1.4225,
|
||||
"num_input_tokens_seen": 44592,
|
||||
"step": 15
|
||||
},
|
||||
{
|
||||
"epoch": 0.032,
|
||||
"grad_norm": 1.720107913017273,
|
||||
"learning_rate": 9.24024048078213e-05,
|
||||
"loss": 1.4217,
|
||||
"num_input_tokens_seen": 52400,
|
||||
"step": 18
|
||||
},
|
||||
{
|
||||
"epoch": 0.037333333333333336,
|
||||
"grad_norm": 0.930574893951416,
|
||||
"learning_rate": 8.596699001693255e-05,
|
||||
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|
||||
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|
||||
"step": 21
|
||||
},
|
||||
{
|
||||
"epoch": 0.042666666666666665,
|
||||
"grad_norm": 1.6979925632476807,
|
||||
"learning_rate": 7.795964517353735e-05,
|
||||
"loss": 1.4875,
|
||||
"num_input_tokens_seen": 67024,
|
||||
"step": 24
|
||||
},
|
||||
{
|
||||
"epoch": 0.048,
|
||||
"grad_norm": 2.2298834323883057,
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||||
"learning_rate": 6.873032967079561e-05,
|
||||
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||||
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|
||||
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||||
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|
||||
{
|
||||
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||||
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|
||||
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|
||||
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|
||||
{
|
||||
"epoch": 0.058666666666666666,
|
||||
"grad_norm": 1.1659108400344849,
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||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"step": 39
|
||||
},
|
||||
{
|
||||
"epoch": 0.07466666666666667,
|
||||
"grad_norm": 0.6766985654830933,
|
||||
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|
||||
"loss": 1.2969,
|
||||
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|
||||
"step": 42
|
||||
},
|
||||
{
|
||||
"epoch": 0.08,
|
||||
"grad_norm": 0.7236246466636658,
|
||||
"learning_rate": 1.1697777844051105e-05,
|
||||
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|
||||
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|
||||
"step": 45
|
||||
},
|
||||
{
|
||||
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|
||||
"grad_norm": 0.8583828806877136,
|
||||
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|
||||
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|
||||
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|
||||
"step": 48
|
||||
}
|
||||
],
|
||||
"logging_steps": 3,
|
||||
"max_steps": 50,
|
||||
"num_input_tokens_seen": 132048,
|
||||
"num_train_epochs": 1,
|
||||
"save_steps": 500,
|
||||
"stateful_callbacks": {
|
||||
"TrainerControl": {
|
||||
"args": {
|
||||
"should_epoch_stop": false,
|
||||
"should_evaluate": false,
|
||||
"should_log": false,
|
||||
"should_save": true,
|
||||
"should_training_stop": true
|
||||
},
|
||||
"attributes": {}
|
||||
}
|
||||
},
|
||||
"total_flos": 5638623387844608.0,
|
||||
"train_batch_size": 2,
|
||||
"trial_name": null,
|
||||
"trial_params": null
|
||||
}
|
||||
Binary file not shown.
|
|
@ -0,0 +1,8 @@
|
|||
{
|
||||
"epoch": 0.08888888888888889,
|
||||
"eval_loss": 1.3352934122085571,
|
||||
"eval_runtime": 54.3905,
|
||||
"eval_samples_per_second": 18.386,
|
||||
"eval_steps_per_second": 9.193,
|
||||
"num_input_tokens_seen": 132048
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{"cur_time": "2024-09-18 16:22:51", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:23:56", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:25:01", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:26:05", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:27:10", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:28:15", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
{"cur_time": "2024-09-18 16:29:19", "npu_power_dissipation": [{"npu_id": 0, "power_dissipation": }, {"npu_id": 1, "power_dissipation": }, {"npu_id": 2, "power_dissipation": }, {"npu_id": 3, "power_dissipation": }, {"npu_id": 4, "power_dissipation": }, {"npu_id": 5, "power_dissipation": }, {"npu_id": 6, "power_dissipation": }, {"npu_id": 7, "power_dissipation": }], "device_mem_usage": []}
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
[2024-09-18 16:23:02,978] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to npu (auto detect)
|
||||
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
|
||||
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
|
||||
09/18/2024 16:23:08 - INFO - llamafactory.hparams.parser - Process rank: 0, device: npu:0, n_gpu: 1, distributed training: False, compute dtype: torch.float16
|
||||
09/18/2024 16:23:09 - INFO - llamafactory.data.template - Add eos token: <|im_end|>
|
||||
09/18/2024 16:23:09 - INFO - llamafactory.data.template - Add pad token: <|im_end|>
|
||||
09/18/2024 16:23:09 - INFO - llamafactory.data.loader - Loading dataset AI-ModelScope/train_1M_CN...
|
||||
training example:
|
||||
input_ids:
|
||||
[151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 104317, 89012, 22382, 106096, 64471, 101137, 72881, 102648, 46448, 1773, 62244, 107132, 37945, 99553, 25177, 101898, 8997, 100431, 99639, 113773, 9370, 111749, 25, 330, 100012, 105435, 99487, 100220, 3837, 104817, 44063, 99553, 102322, 20074, 33108, 116993, 3837, 23031, 104022, 100147, 101313, 1773, 698, 151645, 198, 151644, 77091, 198, 99487, 111749, 101137, 72881, 102648, 46448, 1773, 151645]
|
||||
inputs:
|
||||
<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
<|im_start|>user
|
||||
判断给定的文章是否符合语法规则。如果不符合，请提供修改建议。
|
||||
下面是一篇文章的开头: "为了探讨这个主题，本文将提供一系列数据和实例，以证明这一观点。"
|
||||
<|im_end|>
|
||||
<|im_start|>assistant
|
||||
这个开头符合语法规则。<|im_end|>
|
||||
label_ids:
|
||||
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 99487, 111749, 101137, 72881, 102648, 46448, 1773, 151645]
|
||||
labels:
|
||||
这个开头符合语法规则。<|im_end|>
|
||||
09/18/2024 16:26:20 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
|
||||
09/18/2024 16:26:20 - INFO - llamafactory.model.model_utils.attention - Using vanilla attention implementation.
|
||||
09/18/2024 16:26:20 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
|
||||
09/18/2024 16:26:20 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA
|
||||
09/18/2024 16:26:20 - INFO - llamafactory.model.model_utils.misc - Found linear modules: w2,c_proj,w1,c_attn
|
||||
09/18/2024 16:26:21 - INFO - llamafactory.model.loader - trainable params: 17,891,328 || all params: 7,739,215,872 || trainable%: 0.2312
|
||||
Gradient overflow. Skipping step
|
||||
Loss scaler reducing loss scale to 32768.0
|
||||
{'loss': 1.5189, 'grad_norm': 0.8999722599983215, 'learning_rate': 4e-05, 'epoch': 0.01, 'num_input_tokens_seen': 9808}
|
||||
Gradient overflow. Skipping step
|
||||
Loss scaler reducing loss scale to 16384.0
|
||||
Gradient overflow. Skipping step
|
||||
Loss scaler reducing loss scale to 8192.0
|
||||
{'loss': 1.5504, 'grad_norm': nan, 'learning_rate': 6e-05, 'epoch': 0.01, 'num_input_tokens_seen': 19312}
|
||||
{'loss': 1.5661, 'grad_norm': 0.9268227219581604, 'learning_rate': 9.987820251299122e-05, 'epoch': 0.02, 'num_input_tokens_seen': 29232}
|
||||
{'loss': 1.7033, 'grad_norm': 1.1588999032974243, 'learning_rate': 9.806308479691595e-05, 'epoch': 0.02, 'num_input_tokens_seen': 37984}
|
||||
Gradient overflow. Skipping step
|
||||
Loss scaler reducing loss scale to 4096.0
|
||||
{'loss': 1.4225, 'grad_norm': 1.0571134090423584, 'learning_rate': 9.567727288213005e-05, 'epoch': 0.03, 'num_input_tokens_seen': 44592}
|
||||
Gradient overflow. Skipping step
|
||||
Loss scaler reducing loss scale to 2048.0
|
||||
{'loss': 1.4217, 'grad_norm': 1.720107913017273, 'learning_rate': 9.24024048078213e-05, 'epoch': 0.03, 'num_input_tokens_seen': 52400}
|
||||
{'loss': 1.2793, 'grad_norm': 0.930574893951416, 'learning_rate': 8.596699001693255e-05, 'epoch': 0.04, 'num_input_tokens_seen': 60320}
|
||||
{'loss': 1.4875, 'grad_norm': 1.6979925632476807, 'learning_rate': 7.795964517353735e-05, 'epoch': 0.04, 'num_input_tokens_seen': 67024}
|
||||
{'loss': 1.2446, 'grad_norm': 2.2298834323883057, 'learning_rate': 6.873032967079561e-05, 'epoch': 0.05, 'num_input_tokens_seen': 73776}
|
||||
{'loss': 1.4691, 'grad_norm': 1.6609553098678589, 'learning_rate': 5.868240888334653e-05, 'epoch': 0.05, 'num_input_tokens_seen': 82592}
|
||||
{'loss': 1.4451, 'grad_norm': 1.1659108400344849, 'learning_rate': 4.825502516487497e-05, 'epoch': 0.06, 'num_input_tokens_seen': 90512}
|
||||
{'loss': 1.4139, 'grad_norm': 1.2636826038360596, 'learning_rate': 3.790390522001662e-05, 'epoch': 0.06, 'num_input_tokens_seen': 96848}
|
||||
{'loss': 1.3205, 'grad_norm': 3.8678996562957764, 'learning_rate': 2.8081442660546125e-05, 'epoch': 0.07, 'num_input_tokens_seen': 103728}
|
||||
{'loss': 1.2969, 'grad_norm': 0.6766985654830933, 'learning_rate': 1.9216926233717085e-05, 'epoch': 0.07, 'num_input_tokens_seen': 112160}
|
||||
{'loss': 1.5026, 'grad_norm': 0.7236246466636658, 'learning_rate': 1.1697777844051105e-05, 'epoch': 0.08, 'num_input_tokens_seen': 117984}
|
||||
{'loss': 1.3583, 'grad_norm': 0.8583828806877136, 'learning_rate': 5.852620357053651e-06, 'epoch': 0.09, 'num_input_tokens_seen': 126624}
|
||||
{'train_runtime': 174.5036, 'train_samples_per_second': 4.584, 'train_steps_per_second': 0.287, 'train_tokens_per_second': 1136.939, 'train_loss': 1.4425424814224244, 'epoch': 0.09, 'num_input_tokens_seen': 132048}
|
||||
***** train metrics *****
|
||||
epoch = 0.0889
|
||||
num_input_tokens_seen = 132048
|
||||
total_flos = 5251377GF
|
||||
train_loss = 1.4425
|
||||
train_runtime = 0:02:54.50
|
||||
train_samples_per_second = 4.584
|
||||
train_steps_per_second = 0.287
|
||||
train_tokens_per_second = 1136.939
|
||||
Figure saved at: ./results/lora_sft_Qwen-7B_1_gpu_50_step_20240918162245/training_loss.png
|
||||
09/18/2024 16:29:17 - WARNING - llamafactory.extras.ploting - No metric eval_loss to plot.
|
||||
09/18/2024 16:29:17 - WARNING - llamafactory.extras.ploting - No metric eval_accuracy to plot.
|
||||
***** eval metrics *****
|
||||
epoch = 0.0889
|
||||
eval_loss = 1.3353
|
||||
eval_runtime = 0:00:54.39
|
||||
eval_samples_per_second = 18.386
|
||||
eval_steps_per_second = 9.193
|
||||
num_input_tokens_seen = 132048
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
cutoff_len: 1024
|
||||
dataset: belle_1m
|
||||
ddp_timeout: 180000000
|
||||
do_train: true
|
||||
eval_steps: 500
|
||||
eval_strategy: steps
|
||||
finetuning_type: lora
|
||||
fp16: true
|
||||
gradient_accumulation_steps: 8
|
||||
include_num_input_tokens_seen: true
|
||||
include_tokens_per_second: true
|
||||
learning_rate: 0.0001
|
||||
logging_steps: 3
|
||||
lora_target: all
|
||||
lr_scheduler_type: cosine
|
||||
max_samples: 10000
|
||||
max_steps: 50
|
||||
model_name_or_path: ../../../models/qwen
|
||||
num_train_epochs: 10.0
|
||||
output_dir: ./results/lora_sft_Qwen-7B_1_gpu_50_step_20240918162245
|
||||
overwrite_cache: true
|
||||
overwrite_output_dir: true
|
||||
per_device_eval_batch_size: 2
|
||||
per_device_train_batch_size: 2
|
||||
plot_loss: true
|
||||
preprocessing_num_workers: 16
|
||||
save_steps: 500
|
||||
stage: sft
|
||||
template: qwen
|
||||
val_size: 0.1
|
||||
warmup_ratio: 0.1
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|im_end|>"
|
||||
}
|
||||
|
|
@ -0,0 +1,276 @@
|
|||
# Copyright (c) Alibaba Cloud.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
|
||||
"""Tokenization classes for QWen."""
|
||||
|
||||
import base64
|
||||
import logging
|
||||
import os
|
||||
import unicodedata
|
||||
from typing import Collection, Dict, List, Set, Tuple, Union
|
||||
|
||||
import tiktoken
|
||||
from transformers import PreTrainedTokenizer, AddedToken
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
|
||||
|
||||
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
||||
ENDOFTEXT = "<|endoftext|>"
|
||||
IMSTART = "<|im_start|>"
|
||||
IMEND = "<|im_end|>"
|
||||
# as the default behavior is changed to allow special tokens in
|
||||
# regular texts, the surface forms of special tokens need to be
|
||||
# as different as possible to minimize the impact
|
||||
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
||||
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
||||
SPECIAL_START_ID = 151643
|
||||
SPECIAL_TOKENS = tuple(
|
||||
enumerate(
|
||||
(
|
||||
(
|
||||
ENDOFTEXT,
|
||||
IMSTART,
|
||||
IMEND,
|
||||
)
|
||||
+ EXTRAS
|
||||
),
|
||||
start=SPECIAL_START_ID,
|
||||
)
|
||||
)
|
||||
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
|
||||
|
||||
|
||||
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
||||
with open(tiktoken_bpe_file, "rb") as f:
|
||||
contents = f.read()
|
||||
return {
|
||||
base64.b64decode(token): int(rank)
|
||||
for token, rank in (line.split() for line in contents.splitlines() if line)
|
||||
}
|
||||
|
||||
|
||||
class QWenTokenizer(PreTrainedTokenizer):
|
||||
"""QWen tokenizer."""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
errors="replace",
|
||||
extra_vocab_file=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# how to handle errors in decoding UTF-8 byte sequences
|
||||
# use ignore if you are in streaming inference
|
||||
self.errors = errors
|
||||
|
||||
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
||||
self.special_tokens = {
|
||||
token: index
|
||||
for index, token in SPECIAL_TOKENS
|
||||
}
|
||||
|
||||
# try load extra vocab from file
|
||||
if extra_vocab_file is not None:
|
||||
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
||||
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
||||
for token, index in extra_mergeable_ranks.items():
|
||||
if token in self.mergeable_ranks:
|
||||
logger.info(f"extra token {token} exists, skipping")
|
||||
continue
|
||||
if index in used_ids:
|
||||
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
||||
continue
|
||||
self.mergeable_ranks[token] = index
|
||||
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
||||
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
assert (
|
||||
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
||||
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
||||
|
||||
self.decoder = {
|
||||
v: k for k, v in self.mergeable_ranks.items()
|
||||
} # type: dict[int, bytes|str]
|
||||
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
||||
|
||||
self.tokenizer = enc # type: tiktoken.Encoding
|
||||
|
||||
self.eod_id = self.tokenizer.eot_token
|
||||
self.im_start_id = self.special_tokens[IMSTART]
|
||||
self.im_end_id = self.special_tokens[IMEND]
|
||||
|
||||
def __getstate__(self):
|
||||
# for pickle lovers
|
||||
state = self.__dict__.copy()
|
||||
del state["tokenizer"]
|
||||
return state
|
||||
|
||||
def __setstate__(self, state):
|
||||
# tokenizer is not python native; don't pass it; rebuild it
|
||||
self.__dict__.update(state)
|
||||
enc = tiktoken.Encoding(
|
||||
"Qwen",
|
||||
pat_str=PAT_STR,
|
||||
mergeable_ranks=self.mergeable_ranks,
|
||||
special_tokens=self.special_tokens,
|
||||
)
|
||||
self.tokenizer = enc
|
||||
|
||||
def __len__(self) -> int:
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def get_vocab(self) -> Dict[bytes, int]:
|
||||
return self.mergeable_ranks
|
||||
|
||||
def convert_tokens_to_ids(
|
||||
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
||||
) -> List[int]:
|
||||
ids = []
|
||||
if isinstance(tokens, (str, bytes)):
|
||||
if tokens in self.special_tokens:
|
||||
return self.special_tokens[tokens]
|
||||
else:
|
||||
return self.mergeable_ranks.get(tokens)
|
||||
for token in tokens:
|
||||
if token in self.special_tokens:
|
||||
ids.append(self.special_tokens[token])
|
||||
else:
|
||||
ids.append(self.mergeable_ranks.get(token))
|
||||
return ids
|
||||
|
||||
def _add_tokens(
|
||||
self,
|
||||
new_tokens: Union[List[str], List[AddedToken]],
|
||||
special_tokens: bool = False,
|
||||
) -> int:
|
||||
if not special_tokens and new_tokens:
|
||||
raise ValueError("Adding regular tokens is not supported")
|
||||
for token in new_tokens:
|
||||
surface_form = token.content if isinstance(token, AddedToken) else token
|
||||
if surface_form not in SPECIAL_TOKENS_SET:
|
||||
raise ValueError("Adding unknown special tokens is not supported")
|
||||
return 0
|
||||
|
||||
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
||||
"""
|
||||
Save only the vocabulary of the tokenizer (vocabulary).
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
||||
with open(file_path, "w", encoding="utf8") as w:
|
||||
for k, v in self.mergeable_ranks.items():
|
||||
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
||||
w.write(line)
|
||||
return (file_path,)
|
||||
|
||||
def tokenize(
|
||||
self,
|
||||
text: str,
|
||||
allowed_special: Union[Set, str] = "all",
|
||||
disallowed_special: Union[Collection, str] = (),
|
||||
**kwargs,
|
||||
) -> List[Union[bytes, str]]:
|
||||
"""
|
||||
Converts a string in a sequence of tokens.
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
The sequence to be encoded.
|
||||
allowed_special (`Literal["all"]` or `set`):
|
||||
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
||||
Default to "all".
|
||||
disallowed_special (`Literal["all"]` or `Collection`):
|
||||
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
||||
Default to an empty tuple.
|
||||
|
||||
kwargs (additional keyword arguments, *optional*):
|
||||
Will be passed to the underlying model specific encode method.
|
||||
|
||||
Returns:
|
||||
`List[bytes|str]`: The list of tokens.
|
||||
"""
|
||||
tokens = []
|
||||
text = unicodedata.normalize("NFC", text)
|
||||
|
||||
# this implementation takes a detour: text -> token id -> token surface forms
|
||||
for t in self.tokenizer.encode(
|
||||
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
||||
):
|
||||
tokens.append(self.decoder[t])
|
||||
return tokens
|
||||
|
||||
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
||||
"""
|
||||
Converts a sequence of tokens in a single string.
|
||||
"""
|
||||
text = ""
|
||||
temp = b""
|
||||
for t in tokens:
|
||||
if isinstance(t, str):
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
temp = b""
|
||||
text += t
|
||||
elif isinstance(t, bytes):
|
||||
temp += t
|
||||
else:
|
||||
raise TypeError("token should only be of type types or str")
|
||||
if temp:
|
||||
text += temp.decode("utf-8", errors=self.errors)
|
||||
return text
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self.tokenizer.n_vocab
|
||||
|
||||
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
||||
"""Converts an id to a token, special tokens included"""
|
||||
if index in self.decoder:
|
||||
return self.decoder[index]
|
||||
raise ValueError("unknown ids")
|
||||
|
||||
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
||||
"""Converts a token to an id using the vocab, special tokens included"""
|
||||
if token in self.special_tokens:
|
||||
return self.special_tokens[token]
|
||||
if token in self.mergeable_ranks:
|
||||
return self.mergeable_ranks[token]
|
||||
raise ValueError("unknown token")
|
||||
|
||||
def _tokenize(self, text: str, **kwargs):
|
||||
"""
|
||||
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
||||
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
||||
|
||||
Do NOT take care of added tokens.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def _decode(
|
||||
self,
|
||||
token_ids: Union[int, List[int]],
|
||||
skip_special_tokens: bool = False,
|
||||
errors: str = None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
if isinstance(token_ids, int):
|
||||
token_ids = [token_ids]
|
||||
if skip_special_tokens:
|
||||
token_ids = [i for i in token_ids if i < self.eod_id]
|
||||
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
{
|
||||
"added_tokens_decoder": {},
|
||||
"auto_map": {
|
||||
"AutoTokenizer": [
|
||||
"tokenization_qwen.QWenTokenizer",
|
||||
null
|
||||
]
|
||||
},
|
||||
"chat_template": "{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|im_end|>",
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|im_end|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "QWenTokenizer"
|
||||
}
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{
|
||||
"epoch": 0.08888888888888889,
|
||||
"num_input_tokens_seen": 132048,
|
||||
"total_flos": 5638623387844608.0,
|
||||
"train_loss": 1.4425424814224244,
|
||||
"train_runtime": 174.5036,
|
||||
"train_samples_per_second": 4.584,
|
||||
"train_steps_per_second": 0.287,
|
||||
"train_tokens_per_second": 1136.939
|
||||
}
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
{"current_steps": 3, "total_steps": 50, "loss": 1.5189, "learning_rate": 4e-05, "epoch": 0.005333333333333333, "percentage": 6.0, "cur_time": "2024-09-18 16:26:34", "elapsed_time": "0:00:12", "remaining_time": "0:03:08", "throughput": 815.19, "total_tokens": 9808}
|
||||
{"current_steps": 6, "total_steps": 50, "loss": 1.5504, "learning_rate": 6e-05, "epoch": 0.010666666666666666, "percentage": 12.0, "cur_time": "2024-09-18 16:26:44", "elapsed_time": "0:00:22", "remaining_time": "0:02:43", "throughput": 864.66, "total_tokens": 19312}
|
||||
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Reference in New Issue