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92
README.md
92
README.md
|
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@ -1,27 +1,83 @@
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|||
夸克网盘 docker链接：https://pan.quark.cn/s/4cda395f13e8
|
||||
(没有会员请联系我下载)
|
||||
# 九格通用基础大模型
|
||||
|
||||
## 简介
|
||||
启元九格大模型由启元实验室牵头，联合清华大学、哈尔滨工业大学、中国科学院计算技术研究所、北京大学、南开大学等顶尖科研单位共同研发。该模型具备 **高效训练与推理**、**高效适配与部署** 的技术特点，支持多种 **自然语言处理（NLP）** 和 **多模态** 任务，包括 **文本问答、文本分类、机器翻译、文本摘要、图文理解等**。
|
||||
|
||||
1.使用llama-factory对九格模型进行全参数微调。数据集见dataset
|
||||
---
|
||||
|
||||
2.训练和推理都已验证无误，在A100*8卡机器上。
|
||||
docker 启动：sudo docker run -it --runtime=nvidia --gpus all --shm-size=256g wjf:train
|
||||
推理：python inference.py
|
||||
训练：
|
||||
cd training
|
||||
sh training.sh
|
||||
## 更新信息
|
||||
|
||||
### 🔥 最新版本（2025.01.12）：[**FM9G-V**](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/FM9G-V/quick_start_clean/readmes/quick_start.md)
|
||||
- **模型**：**13B 多模态基础大模型**，支持 **单图文推理**。
|
||||
- **训练**：开源了 **多模态基础大模型** 的训练代码。
|
||||
- **推理**：支持 **单图文推理**。
|
||||
|
||||
3.推理使用多checkpoint、多次推理融合。
|
||||
### 🚀 历史更新（2024.08.19）：[**FM9G**](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/FM_9G/quick_start_clean/readmes/quick_start.md)
|
||||
- **2B 模型** 经过多数据集验证，发现 **LoRA 训练效果不及全参数微调**，因此 2B 采用 **全参数微调** 训练。
|
||||
- **8B 模型** LoRA 微调仍在 **master 分支** 进行训练。
|
||||
- **QUICK START** 中更新了 **2B 全参数微调** 的详细信息。
|
||||
|
||||
4.所有资料都已打包进docker，只需要docker即可。
|
||||
---
|
||||
|
||||
5.启动训练时将覆盖提交的checkpoint。
|
||||
### 📚 其他信息
|
||||
- 若仍在使用旧版本的九格模型训练和推理，请切换分支至 [master](https://www.osredm.com/jiuyuan/CPM-9G-8B/tree/master/quick_start_clean/readmes/README_ALL.md) 分支。
|
||||
|
||||
6.docker卡在数据处理可能是机器的问题，尝试docker中输入：
|
||||
export NCCL_DEBUG=INFO
|
||||
export NCCL_SHM_DISABLE=1
|
||||
export NCCL_P2P_DISABLE=1
|
||||
由于需要保存多个checkpoint，请务必保证磁盘空间足够，大于500G。
|
||||
---
|
||||
|
||||
7.提交不易，请有问题是及时联系我（电话：）
|
||||
### 📌 开源模型参数级别
|
||||
|
||||
| 模型 | 主要能力 | 参数规模 | 代码分支 |
|
||||
|---------------|-------------------|--------|--------|
|
||||
| **FM9G-8B** | **文本处理（NLP）** | 80 亿 | FM9G |
|
||||
| **FM9G-2B** | **文本处理（NLP）** | 20 亿 | FM9G |
|
||||
| **FM9G-V（13B）** | **多模态（文本+图像）** | 130 亿 | FM9G-V |
|
||||
|
||||
|
||||
# 迈向通用智能的大模型技术系列课程
|
||||
系列课程全方位介绍人工智能和大模型技术的基础知识和前沿课题，理论学习和实践应用相结合。课程既有“人工智能与大模型通论”和“神经网络与预训练模型”等基础知识，也有“九格大模型生态体系”和“领域大模型实战”等实战主题，基本内容包括大模型训练、微调、知识增强、伦理安全、多模态、具身智能、自主智能体等话题，高级选题包括多语言处理、面向科学研究的大模型应用、高效计算技术、评测与数据科学等话题。课程旨在通过一系列精心设计的单元为学习者提供大型通用人工智能的学习之旅。
|
||||
|
||||
## 人工智能大模型通论
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%A8%A1%E5%9E%8B%E9%80%9A%E8%AE%BA-%E5%AD%99%E8%8C%82%E6%9D%BE%E8%80%81%E5%B8%88-1124_DeWatermark.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[人工智能与大模型通论-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/1.%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%A8%A1%E5%9E%8B%E9%80%9A%E8%AE%BA-PPT.pdf)
|
||||
|
||||
## 大模型技术的重要特性与发展趋势
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8A%80%E6%9C%AF%E7%9A%84%E9%87%8D%E8%A6%81%E7%89%B9%E6%80%A7%E4%B8%8E%E5%8F%91%E5%B1%95%E8%B6%8B%E5%8A%BF-%E5%88%98%E7%9F%A5%E8%BF%9C%E8%80%81%E5%B8%88-1201_DeWatermark.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[大模型技术的重要特性与发展趋势-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/2.%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8A%80%E6%9C%AF%E7%9A%84%E9%87%8D%E8%A6%81%E7%89%B9%E6%80%A7%E4%B8%8E%E5%8F%91%E5%B1%95%E8%B6%8B%E5%8A%BF-PPT.pdf)
|
||||
|
||||
## 大语言模型的适配与对齐技术
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/2023-12-22-%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E9%80%82%E9%85%8D%E4%B8%8E%E5%AF%B9%E9%BD%90%E6%8A%80%E6%9C%AF-%E4%B8%81%E5%AE%81_DeWatermark.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[大语言模型的适配与对齐技术-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/3.%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E7%9A%84%E9%80%82%E9%85%8D%E4%B8%8E%E5%AF%B9%E9%BD%90%E6%8A%80%E6%9C%AF-PPT.pdf)
|
||||
|
||||
## 大模型领域适配原理与实战
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/2023-12-29%E5%A4%A7%E6%A8%A1%E5%9E%8B%E9%A2%86%E5%9F%9F%E9%80%82%E9%85%8D%E5%8E%9F%E7%90%86%E4%B8%8E%E5%AE%9E%E6%88%98-%E7%8E%8B%E7%A1%95_DeWatermark.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[大模型领域适配原理与实战-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/4.%E5%A4%A7%E6%A8%A1%E5%9E%8B%E9%A2%86%E5%9F%9F%E9%80%82%E9%85%8D%E5%8E%9F%E7%90%86%E4%B8%8E%E5%AE%9E%E6%88%98-PPT.pdf)
|
||||
|
||||
## 知识增强的大语言模型
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E7%9F%A5%E8%AF%86%E5%A2%9E%E5%BC%BA%E7%9A%84%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[知识增强的大语言模型-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/5.%E6%A3%80%E7%B4%A2%E5%A2%9E%E5%BC%BA%E7%9A%84%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B-PPT.pdf)
|
||||
|
||||
## 大模型工具学习
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%B7%A5%E5%85%B7%E5%AD%A6%E4%B9%A0.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[大模型工具学习-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/6.%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%B7%A5%E5%85%B7%E5%AD%A6%E4%B9%A0-PPT.pdf)
|
||||
|
||||
## 检索增强生成的基本实现
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E6%A3%80%E7%B4%A2%E5%A2%9E%E5%BC%BA%E7%94%9F%E6%88%90%E7%9A%84%E5%9F%BA%E6%9C%AC%E5%AE%9E%E7%8E%B0.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[检索增强生成的基本实现-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/7.%E6%A3%80%E7%B4%A2%E5%A2%9E%E5%BC%BA%E7%94%9F%E6%88%90%E7%9A%84%E5%9F%BA%E6%9C%AC%E5%AE%9E%E7%8E%B0-PPT.pdf)
|
||||
|
||||
## 多模态语义检索与检索增强技术
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/%E5%A4%9A%E6%A8%A1%E6%80%81%E8%AF%AD%E4%B9%89%E6%A3%80%E7%B4%A2%E4%B8%8E%E6%A3%80%E7%B4%A2%E5%A2%9E%E5%BC%BA%E6%8A%80%E6%9C%AF.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[多模态语义检索与检索增强技术-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/8.%E5%A4%9A%E6%A8%A1%E6%80%81%E8%AF%AD%E4%B9%89%E6%A3%80%E7%B4%A2%E4%B8%8E%E6%A3%80%E7%B4%A2%E5%A2%9E%E5%BC%BA%E6%8A%80%E6%9C%AF-PPT.pdf)
|
||||
|
||||
## 大语言模型驱动的多智能体协作与演化
|
||||
<video src="https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/400_0121.mp4
|
||||
" width="800px" height="600px" controls="controls"></video>
|
||||
[大语言模型驱动的多智能体协作与演化-PPT](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/%E8%AF%BE%E7%A8%8B%E8%A7%86%E9%A2%91/9.%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E9%A9%B1%E5%8A%A8%E7%9A%84%E5%A4%9A%E6%99%BA%E8%83%BD%E4%BD%93%E5%8D%8F%E4%BD%9C%E4%B8%8E%E6%BC%94%E5%8C%96-PPT.pdf)
|
||||
|
||||
278
inference.py
278
inference.py
|
|
@ -1,278 +0,0 @@
|
|||
import json, torch, re, sys, subprocess
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel, StoppingCriteria
|
||||
device = "cuda" # the device to load the model onto
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def exec_code(test):
|
||||
with open("test_case.json", "r") as f:
|
||||
test_cases = json.load(f)
|
||||
|
||||
right_num = 0
|
||||
all_num = 0
|
||||
package = "import os, sys, math, re, json, random\n"
|
||||
|
||||
for item, test_case in zip(test, test_cases):
|
||||
|
||||
|
||||
if "```python\n" in item["raw_outputs"]:
|
||||
matches = re.findall('```python(.*?)```', item["raw_outputs"], re.DOTALL)
|
||||
if len(matches) == 1:
|
||||
item["raw_outputs"] = matches[0]
|
||||
else:
|
||||
matches = re.findall('```python(.*?)assert', item["raw_outputs"], re.DOTALL)
|
||||
if len(matches) == 1:
|
||||
item["raw_outputs"] = matches[0]
|
||||
else:
|
||||
item["raw_outputs"] = item["raw_outputs"][item["raw_outputs"].index("python\n") + len("python\n"):]
|
||||
print(item)
|
||||
#break
|
||||
|
||||
|
||||
code = item["raw_outputs"].replace("<|im_end|>", "").replace("</s>", "").replace("```", "").strip().rstrip("\n")
|
||||
|
||||
raw_code = code
|
||||
|
||||
codes = raw_code.split("\n")
|
||||
last_line = 0
|
||||
for index, line in enumerate(codes):
|
||||
if " return" in line:
|
||||
last_line = index
|
||||
|
||||
code = "\n".join(codes[:last_line+1])
|
||||
|
||||
|
||||
'''
|
||||
if raw_code != code:
|
||||
print("\n--------------------------------------------------------\n", [raw_code], "\n--------------------------------------------------------\n")
|
||||
print("clean:\n", [code], "\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n\n")
|
||||
'''
|
||||
|
||||
|
||||
with open('code_.py', 'w') as fout:
|
||||
fout.write(package + code + "\n" + "\n".join(test_case["test_case"]))
|
||||
|
||||
batcmd = 'timeout 3 ' + sys.executable + ' code_.py'
|
||||
try:
|
||||
shell_output = subprocess.check_output(batcmd, shell=True).decode('utf8')
|
||||
right_num += 1
|
||||
item["result"] = "True"
|
||||
except Exception as e:
|
||||
print("++++++++++++++++++++++++++++++++++++++++++++++++++++\n", raw_code, "\n-----------------------------------------\n\n\n", package + code + "\n--------------------------\n" + "\n".join(test_case["test_case"]))
|
||||
print("--------------------------------------------------------\n\n\nitem:", item)
|
||||
print("e: ", e, "\n================================================\n")#, e, )
|
||||
item["result"] = "False"
|
||||
|
||||
all_num += 1
|
||||
|
||||
item["raw_outputs"] = [code]
|
||||
|
||||
print(len(test), right_num, all_num, right_num / all_num)
|
||||
with open(f'wjf_{model_path.replace("/", "-")}{right_num / all_num}.json', "w") as f:
|
||||
json.dump(test, f, indent=4)
|
||||
|
||||
return test, right_num / all_num
|
||||
|
||||
|
||||
|
||||
def get_result(model, tokenizer):
|
||||
|
||||
test = []
|
||||
with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
|
||||
#test = json.load(f)
|
||||
for line in f:
|
||||
test.append(json.loads(line))
|
||||
|
||||
all_score = 0
|
||||
all_num = 0
|
||||
test_num = 1000
|
||||
|
||||
|
||||
from tqdm import tqdm
|
||||
for example in tqdm(test[:]):
|
||||
#print(example["question"])
|
||||
example["question"] = example["question"].replace("'''", '"""')
|
||||
|
||||
ai_prefix = ""
|
||||
if example["question"].split(" ")[0] == "Write":
|
||||
question = example["question"][:example["question"].index("\n")].strip().rstrip()
|
||||
test_case = example["question"][example["question"].index("\n"):].split("\n")
|
||||
print("test_case: ", test_case)
|
||||
|
||||
function_name = test_case[1].split(" ")[1].split("(")[0]
|
||||
ai_prefix = "def " + function_name
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": question + "\n\n" + ("\n".join(test_case))}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
text += ai_prefix
|
||||
example["test_case"] = test_case
|
||||
|
||||
|
||||
else:
|
||||
tmp = re.findall(r'"""(.*?)"""', example["question"], flags=re.DOTALL)[0].split("\n")
|
||||
question = ""
|
||||
for line in tmp:
|
||||
line = line.strip().rstrip()
|
||||
if len(line) == 0:
|
||||
continue
|
||||
|
||||
#if "xample" in line and len(line) < 20:
|
||||
# break
|
||||
|
||||
question += line + " "
|
||||
|
||||
code = re.sub(r'"""(.*?)"""', '', example["question"], flags=re.DOTALL).strip().rstrip()
|
||||
ai_prefix = code
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": question}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
text += ai_prefix
|
||||
|
||||
|
||||
example["prompt"] = text
|
||||
print("text: " , [text])
|
||||
input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
|
||||
|
||||
|
||||
output = model.generate(input_ids,
|
||||
#top_p=1.0,
|
||||
max_new_tokens=600,
|
||||
#repetition_penalty=1.1 + t*0.01,
|
||||
temperature=0.1,
|
||||
#no_repeat_ngram_size = 5,
|
||||
).squeeze()
|
||||
|
||||
output_str = tokenizer.decode(output[input_ids.shape[1]:])
|
||||
output_str = ai_prefix + output_str
|
||||
print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
|
||||
|
||||
example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
|
||||
return test
|
||||
|
||||
|
||||
|
||||
def get_result_1(model, tokenizer):
|
||||
|
||||
test = []
|
||||
with open("/mnt/disk2/home/wujianfeng/com/code/code_round4.jsonl", "r") as f:
|
||||
#test = json.load(f)
|
||||
for line in f:
|
||||
test.append(json.loads(line))
|
||||
|
||||
all_score = 0
|
||||
all_num = 0
|
||||
test_num = 1000
|
||||
|
||||
|
||||
from tqdm import tqdm
|
||||
for example in tqdm(test[:]):
|
||||
#print(example["question"])
|
||||
messages = [
|
||||
{"role": "user", "content": example["question"]}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
|
||||
example["prompt"] = text
|
||||
print("text: " , [text])
|
||||
input_ids = tokenizer([text], return_tensors="pt").to(device).input_ids
|
||||
|
||||
|
||||
output = model.generate(input_ids,
|
||||
#top_p=1.0,
|
||||
max_new_tokens=600,
|
||||
#repetition_penalty=1.1 + t*0.01,
|
||||
temperature=0.1,
|
||||
#no_repeat_ngram_size = 5,
|
||||
).squeeze()
|
||||
output_str = tokenizer.decode(output[input_ids.shape[1]:])
|
||||
print("output_str:\n", output_str, "\n-----------------------------------------------------------------")
|
||||
|
||||
example["raw_outputs"] = output_str#re.findall(r'```python(.*?)```', output_str)
|
||||
return test
|
||||
|
||||
|
||||
answers = {}
|
||||
|
||||
for model_path in [
|
||||
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all/TACO/",
|
||||
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_2/CodeNet4Repair/",
|
||||
"/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_1/CodeExercise-Python-27k/",
|
||||
]:
|
||||
print("model_path: ", model_path)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_path,
|
||||
torch_dtype="auto",
|
||||
device_map=device,
|
||||
trust_remote_code=True,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
test = get_result(model, tokenizer)
|
||||
test, score = exec_code(test)
|
||||
answers[score] = test
|
||||
|
||||
|
||||
test = get_result_1(model, tokenizer)
|
||||
test, score = exec_code(test)
|
||||
answers[score] = test
|
||||
|
||||
'''
|
||||
import os
|
||||
for path in os.listdir("./"):
|
||||
if "home-wujianfeng" in path:
|
||||
with open(path, "r") as f:
|
||||
test = json.load(f)
|
||||
answers[float(path.split(".")[-2].split("-")[-1])] = test
|
||||
'''
|
||||
|
||||
answers = list(dict(sorted(answers.items())).values())
|
||||
print("answers: ", answers)
|
||||
right = 0
|
||||
jiuge_right = 0
|
||||
merge = []
|
||||
for i in range(len(answers[0])):
|
||||
#for i in range(2):
|
||||
flag = 0
|
||||
for answer in answers:
|
||||
if answer[i]["result"] == "True":
|
||||
right += 1
|
||||
jiuge_right += 1
|
||||
flag = 1
|
||||
merge.append(answer[i])
|
||||
break
|
||||
|
||||
if flag == 0:
|
||||
merge.append(answers[0][i])
|
||||
|
||||
|
||||
|
||||
print(right / len(answers[0]), jiuge_right / len(answers[0]))
|
||||
with open("wjf_jiuge.jsonl", "w") as f:
|
||||
for item in merge:
|
||||
item.pop("result")
|
||||
f.write(json.dumps(item, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
model_wight:通过百度网盘分享的文件：
|
||||
链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
||||
提取码：6666
|
||||
#https://www.alipan.com/s/FTPWUSBuz7s
|
||||
|
||||
docker:
|
||||
链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
||||
提取码：6666
|
||||
#https://www.alipan.com/s/FTPWUSBuz7s
|
||||
|
||||
train_data:
|
||||
链接：https://pan.baidu.com/s/1paYNO7d5OYESuyw3BVo7Ew
|
||||
提取码：6666
|
||||
#https://www.alipan.com/s/FTPWUSBuz7s
|
||||
|
|
@ -107,9 +107,10 @@ pip install sentencepiece
|
|||
pip install protobuf==3.20.0 #protobuf版本过高时无法适配tensorboard
|
||||
pip install tensorboard
|
||||
pip install tensorboardX
|
||||
|
||||
|
||||
9.安装vllm（模型推理）
|
||||
```
|
||||
### 推理环境安装
|
||||
```
|
||||
装vllm（模型推理）
|
||||
我们提供基于CUDA12.2环境下python3.8、python3.10版本的vllm安装包，相关依赖均已封装，可直接安装后执行推理：
|
||||
[vllm-0.5.0.dev0+cu122-cp38-cp38-linux_x86_64.whl](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/vllm-0.5.0.dev0%2Bcu122-cp38-cp38-linux_x86_64.whl)
|
||||
[vllm-0.5.0.dev0+cu122-cp310-cp310-linux_x86_64.whl](https://qy-obs-6d58.obs.cn-north-4.myhuaweicloud.com/vllm-0.5.0.dev0%2Bcu122-cp310-cp310-linux_x86_64.whl)
|
||||
|
|
|
|||
4099
test_case.json
4099
test_case.json
File diff suppressed because it is too large
Load Diff
154
train.sh
154
train.sh
|
|
@ -1,154 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset TACO \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir TACO \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset Tested-143k-Python-Alpaca \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir Tested-143k-Python-Alpaca \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset UltraInteract_sft \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir UltraInteract_sft \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset code_instructions_120k_alpaca \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir code_instructions_120k_alpaca \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset CodeExercise-Python-27k \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir CodeExercise-Python-27k \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
|
||||
|
||||
deepspeed --include localhost:0,1,2,3,4,5,6,7 --master_port 21666 src/train.py \
|
||||
--stage sft \
|
||||
--model_name_or_path /mnt/diskhd/Backup/DownloadModel/2b_sft_model/ \
|
||||
--do_train \
|
||||
--dataset CodeNet4Repair \
|
||||
--template jiuge \
|
||||
--finetuning_type full \
|
||||
--output_dir CodeNet4Repair \
|
||||
--per_device_train_batch_size 14 \
|
||||
--gradient_accumulation_steps 6 \
|
||||
--lr_scheduler_type cosine \
|
||||
--logging_step 1 \
|
||||
--save_steps 300 \
|
||||
--lr_scheduler_type cosine_with_restarts \
|
||||
--warmup_ratio 0.001 \
|
||||
--optim adamw_torch \
|
||||
--learning_rate 2e-5 \
|
||||
--num_train_epochs 2.0 \
|
||||
--plot_loss \
|
||||
--bf16 \
|
||||
--gradient_checkpointing \
|
||||
--report_to tensorboard \
|
||||
--deepspeed deepspeed_configs/zero2.json \
|
||||
--cutoff_len 2048
|
||||
Loading…
Reference in New Issue