Evaluation
How to use
Hugggingface
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("lightblue/karasu-7B-chat-plus-unleashed")
model = AutoModelForCausalLM.from_pretrained("lightblue/karasu-7B-chat-plus-unleashed", torch_dtype=torch.bfloat16, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}]
messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"})
prompt = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)
pipe(prompt, max_new_tokens=100, do_sample=False, temperature=0.0, return_full_text=False)
VLLM
from vllm import LLM, SamplingParams
sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
llm = LLM(model="lightblue/karasu-7B-chat-plus-unleashed")
messages = [{"role": "system", "content": "あなたはAIアシスタントです。"}]
messages.append({"role": "user", "content": "イギリスの首相は誰ですか？"})
prompt = llm.llm_engine.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)
prompts = [prompt]
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
Base checkpoint
Training datasets (total ~7B)
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
- Lightblue's suite of Kujira datasets (unreleased)
- Lightblue's own question-based datasets (unreleased)
- Lightblue's own category-based datasets (unreleased)
- OASST (Japanese chats only)
- ShareGPT (Japanese chats only)
- augmxnt/ultra-orca-boros-en-ja-v1 (['airoboros', 'slimorca', 'ultrafeedback', 'airoboros_ja_new'] only)
Developed by
Engineers
Peter Devine
Sho Higuchi
Advisors
Yuuki Yamanaka
Atom Sonoda
Project manager
Shunichi Taniguchi
Dataset evaluator
Renju Aoki