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from typing import Iterator |
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model_id = 'xuqinyang/baichuan-13b-chat-ggml-int4' |
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from huggingface_hub import snapshot_download,hf_hub_download |
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snapshot_download(model_id, local_dir="./",revision="b2414a0ceee68fe09c99ace44446cfc9a1c52b08") |
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hf_hub_download(repo_id="baichuan-inc/Baichuan-13B-Chat",local_dir="./", filename="tokenizer.model") |
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from llama_cpp import Llama |
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llm = Llama(model_path="./ggml-model-q4_0.bin", n_ctx=4096,seed=-1) |
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def run(message: str, |
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chat_history: list[tuple[str, str]], |
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system_prompt: str, |
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max_new_tokens: int = 1024, |
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temperature: float = 0.3, |
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top_p: float = 0.85, |
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top_k: int = 5) -> Iterator[str]: |
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history = [] |
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print(chat_history) |
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result="" |
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for i in chat_history: |
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history.append({"role": "user", "content": i[0]}) |
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history.append({"role": "assistant", "content": i[1]}) |
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print(history) |
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history.append({"role": "user", "content": message}) |
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for response in llm.create_chat_completion(history,stop=["</s>"],stream=True,max_tokens=-1,temperature=temperature,top_k=top_k,top_p=top_p,repeat_penalty=1.1): |
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if "content" in response["choices"][0]["delta"]: |
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result = result + response["choices"][0]["delta"]["content"] |
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yield result |
