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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- Hare |
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datasets: |
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- cerebras/SlimPajama-627B |
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- HuggingFaceTB/cosmopedia |
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arxiv: 2406.11410v1 |
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--- |
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<a id="english"></a> |
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<p align="center"> |
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<img width="400px" alt="Lite-AI" src="./logo.jpg"> |
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</p> |
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</div> |
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## Hare-1.1B-Tool |
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- Hare-1.1B-Tool is a fine-tuned version of [Hare-1.1B-base](https://huggingface.co/LiteAI/Hare-1.1B-base), designed to enable the invocation of Android system APIs and tool orchestration in composite scenarios on mobile devices. For a detailed introduction, please refer to [Hare-1.1B-base](https://huggingface.co/LiteAI/Hare-1.1B-base). |
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- Hare-1.1B-Tool是由[Hare-1.1B-base](https://huggingface.co/LiteAI/Hare-1.1B-base)微调而来,用于在手机端实现安卓系统API调用和组合场景下的工具调用。详细介绍请看[Hare-1.1B-base](https://huggingface.co/LiteAI/Hare-1.1B-base)。 |
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## 模型使用 |
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```python |
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import time |
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from transformers import GenerationConfig |
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, StoppingCriteria, StoppingCriteriaList |
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import os |
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import json |
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import logging |
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import torch |
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log_path = 'your_log_path' |
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logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') |
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logging.info('This is a log message.') |
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model_path = "/LiteAI/Hare-1.1B-Tool" |
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tokenizer = AutoTokenizer.from_pretrained(save_path) |
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model = AutoModelForCausalLM.from_pretrained(save_path) |
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class MyStoppingCriteria(StoppingCriteria): |
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
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keyword = tokenizer.decode(input_ids[0][-1]) |
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return keyword in ["<api_end>"] |
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def chat( |
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messages, |
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model, |
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tokenizer, |
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generate_config=None, |
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max_length=512, |
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max_new_tokens=256, |
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): |
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if generate_config is None: |
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generate_config = GenerationConfig( |
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do_sample=False, |
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max_length=max_length, |
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max_new_tokens=max_new_tokens, |
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eos_token_id=32001, |
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) |
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if messages[0]["role"] == "system": |
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system = messages[0]["content"] |
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messages = messages[0:] |
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else: |
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system = "You are a helpful assistant." |
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n_token = max_length |
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system = "<round_start>system\n{}<round_end>\n".format(system) |
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system_token = tokenizer.encode(system, add_special_tokens=False) |
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n_token -= len(system_token) |
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query = messages[-1]["content"] |
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query = "<round_start>user\n{}<round_end>\n<round_start>assistant\n".format(query) |
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query_token = tokenizer.encode(query, add_special_tokens=False) |
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n_token -= len(query_token) |
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messages = messages[:-1] |
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conversations = [] |
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for ids in range(len(messages)-1, 0, -2): |
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user = messages[ids - 1]["content"] |
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assistant = messages[ids]["content"] |
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round = "<round_start>user\n{}<round_end>\n<round_start>assistant\n{}<round_end>\n".format(user, assistant) |
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round_token = tokenizer.encode(round, add_special_tokens=False) |
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if n_token - len(round_token) > 0: |
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conversations = [round] + conversations |
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else: |
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break |
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prompt = system + "".join(conversations) + query |
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prompt_token = tokenizer(prompt, add_special_tokens=False, return_tensors="pt") |
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prompt_token.to(model.device) |
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response = model.generate( |
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generation_config=generate_config, |
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**prompt_token |
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) |
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output_tokens = response[0].cpu().numpy()[prompt_token.input_ids.size()[1]:] |
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output_string = tokenizer.decode(output_tokens, skip_special_tokens=True).replace("<round_end>", "") |
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return output_string, prompt |
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# ====================== |
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# main |
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# ====================== |
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test_query_path = "/home/sxw/sft_exper/dataset/query_to_test" |
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for file in os.listdir(test_query_path): |
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file_pth = os.path.join(test_query_path, file) |
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print(file_pth) |
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logging.info(file_pth) |
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with open(file_pth, 'r') as f: |
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for line in f: |
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data = json.loads(line) |
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# print(data) |
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query = data["human"] |
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messages = [ |
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{"role": "system", "content": "Below is the query from the users, you need make full sense of user's intention based on the content of the sentence, then call the correct function and generate the parameters of the calling function."}, |
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{"role": "user", "content": query} |
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] |
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response, input_prompt = chat(messages=messages, model=model, tokenizer=tokenizer) |
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logging.info(query) |
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logging.info(data["assistant"]) |
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logging.info(response) |
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``` |