97 lines
2.7 KiB
Markdown
97 lines
2.7 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- graelo/wikipedia
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- uonlp/CulturaX
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- HuggingFaceH4/ultrachat_200k
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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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# Evaluation
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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/karasu-7B")
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model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-7B", torch_dtype=torch.bfloat16, device_map="auto")
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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/karasu-7B")
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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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augmxnt/shisa-7b-v1
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* Mistral-7B base
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* Pre-trained on 8B of MADLAD-Ja
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* Finetuned on Japanese instructions
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* Highest scoring 7B model on conversation benchmark (JA MT-Bench)
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# Training datasets (total ~7B)
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* Aozora Bunko
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* Japanese Law Precedent Dataset
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* Japanese Wikipedia
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* .lg.jp, .go.jp, .ac.jp domain webscrapes from CulturaX (Any documents with same first 25 characters were de-duplicated)
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* English Ultrachat200K-gen (So that it doesn't forget English and chatting ability learned in the base checkpoint)
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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 |