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--- |
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license: other |
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license_name: tongyi-qianwen-license-agreement |
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license_link: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT |
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datasets: |
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- OpenAssistant/oasst1 |
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- zetavg/ShareGPT-Processed |
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- augmxnt/ultra-orca-boros-en-ja-v1 |
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language: |
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- ja |
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- en |
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--- |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64c8a2e01c25d2c581a381c1/9CbN4lDGU42c-7DmK_mGM.png" alt="drawing" width="600"/> |
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</p> |
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Qwen/Qwen-14B-Chat + Karasu's finetuning datasets |
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# Demo ・ モデルのデモ |
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[Model demo ・ モデルのデモ](https://lightblue-qarasu.serveo.net/) |
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# Blog post・説明の記事 |
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[Blog post・説明の記事](https://note.com/peter_lightblue/n/ne08a7c8cc47a) |
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# Evaluation |
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In our internal evaluations, we find the Qarasu model to have particularly high performance on the MTーBench benchmark. We are currently awaiting external evaluations. |
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# How to use |
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### Hugggingface |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("lightblue/qarasu-14B-chat-plus-unleashed", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("lightblue/qarasu-14B-chat-plus-unleashed", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}] |
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messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"}) |
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prompt = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) |
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pipe(prompt, max_new_tokens=100, do_sample=False, temperature=0.0, return_full_text=False) |
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``` |
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### VLLM |
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```python |
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from vllm import LLM, SamplingParams |
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sampling_params = SamplingParams(temperature=0.0, max_tokens=100) |
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llm = LLM(model="lightblue/qarasu-14B-chat-plus-unleashed", trust_remote_code=True) |
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messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}] |
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messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"}) |
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prompt = llm.llm_engine.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False) |
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prompts = [prompt] |
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outputs = llm.generate(prompts, sampling_params) |
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for output in outputs: |
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prompt = output.prompt |
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generated_text = output.outputs[0].text |
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") |
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``` |
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# Base checkpoint |
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[Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) |
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# Training datasets (total ~7B) |
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The same as the 'plus' checkpoint, but with about 6K refusals ("申し訳ありませんが、。。。") filtered out from the category dataset |
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* Lightblue's suite of Kujira datasets (unreleased) |
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* Lightblue's own question-based datasets (unreleased) |
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* Lightblue's own category-based datasets (unreleased) |
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* [OASST](https://huggingface.co/datasets/OpenAssistant/oasst1) (Japanese chats only) |
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* [ShareGPT](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) (Japanese chats only) |
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* [augmxnt/ultra-orca-boros-en-ja-v1](https://huggingface.co/datasets/augmxnt/ultra-orca-boros-en-ja-v1) (['airoboros', 'slimorca', 'ultrafeedback', 'airoboros_ja_new'] only) |
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# Developed by |
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<a href="https://www.lightblue-tech.com"> |
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<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/> |
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</a> |
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### Engineers |
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Peter Devine |
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Sho Higuchi |
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### Advisors |
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Yuuki Yamanaka |
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Atom Sonoda |
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### Project manager |
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Shunichi Taniguchi |
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### Dataset evaluator |
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Renju Aoki |
