add llava 1k datasets

This commit is contained in:
hiyouga 2024-05-27 19:57:33 +08:00
parent 30e1c8e745
commit 08bd0440b5
3 changed files with 32 additions and 2 deletions

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@ -107,7 +107,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details. [24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.
[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall`. [24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`.
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details. [23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.

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@ -107,7 +107,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
[24/02/05] Qwen1.5Qwen2 测试版)系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。 [24/02/05] Qwen1.5Qwen2 测试版)系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。
[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**,微调时指定 `dataset: glaive_toolcall` 即可使模型获得工具调用能力。 [24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**,微调时指定 `dataset: glaive_toolcall_zh` 即可使模型获得工具调用能力。
[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `use_unsloth: true` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度,详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。 [23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `use_unsloth: true` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度,详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。

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@ -248,6 +248,36 @@
"ruozhiba_gpt4": { "ruozhiba_gpt4": {
"hf_hub_url": "hfl/ruozhiba_gpt4_turbo" "hf_hub_url": "hfl/ruozhiba_gpt4_turbo"
}, },
"llava_1k_en": {
"hf_hub_url": "BUAADreamer/llava-en-zh-2k",
"subset": "en",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"images": "images"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
}
},
"llava_1k_zh": {
"hf_hub_url": "BUAADreamer/llava-en-zh-2k",
"subset": "zh",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"images": "images"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
}
},
"llava_150k_en": { "llava_150k_en": {
"hf_hub_url": "BUAADreamer/llava-en-zh-300k", "hf_hub_url": "BUAADreamer/llava-en-zh-300k",
"subset": "en", "subset": "en",