118 lines
3.6 KiB
Markdown
118 lines
3.6 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- BAAI/IndustryInstruction_Artificial-Intelligence
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- BAAI/IndustryInstruction
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base_model:
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- meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- 人工智能领域语言模型
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- 机器学习领域语言模型
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---
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This model is finetuned on the model llama3.1-8b-instruct using the dataset [BAAI/IndustryInstruction_Artificial-Intelligence](https://huggingface.co/datasets/BAAI/IndustryInstruction_Artificial-Intelligence) dataset, the dataset details can jump to the repo: [BAAI/IndustryInstruction](https://huggingface.co/datasets/BAAI/IndustryInstruction)
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## training params
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The training framework is llama-factory, template=llama3
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```
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learning_rate=1e-5
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lr_scheduler_type=cosine
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max_length=2048
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warmup_ratio=0.05
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batch_size=64
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epoch=10
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```
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select best ckpt by the evaluation loss
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## evaluation
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Duto to there is no evaluation benchmark, we can not eval the model
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## How to use
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```python
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# !/usr/bin/env python
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# -*- coding:utf-8 -*-
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# ==================================================================
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# [Author] : xiaofeng
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# [Descriptions] :
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# ==================================================================
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import transformers
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import torch
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llama3_jinja = """{% if messages[0]['role'] == 'system' %}
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{% set offset = 1 %}
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{% else %}
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{% set offset = 0 %}
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{% endif %}
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{{ bos_token }}
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{% for message in messages %}
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{% if (message['role'] == 'user') != (loop.index0 % 2 == offset) %}
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{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
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{% endif %}
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{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}
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{% endfor %}
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{% if add_generation_prompt %}
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{{ '<|start_header_id|>' + 'assistant' + '<|end_header_id|>\n\n' }}
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{% endif %}"""
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dtype = torch.bfloat16
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model_dir = "MonteXiaofeng/Artificial-llama3_1_8B_instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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device_map="cuda",
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torch_dtype=dtype,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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tokenizer.chat_template = llama3_jinja # update template
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message = [
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{"role": "system", "content": "You are a helpful assistant"},
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{
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"role": "user",
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"content": "请解释为什么ChatGPT的英文输出结果的逻辑性和准确性远大于中文结果？",
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},
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]
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prompt = tokenizer.apply_chat_template(
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message, tokenize=False, add_generation_prompt=True
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)
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print(prompt)
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inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
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prompt_length = len(inputs[0])
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print(f"prompt_length:{prompt_length}")
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generating_args = {
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"do_sample": True,
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"temperature": 1.0,
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"top_p": 0.5,
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"top_k": 15,
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"max_new_tokens": 512,
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}
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generate_output = model.generate(input_ids=inputs.to(model.device), **generating_args)
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response_ids = generate_output[:, prompt_length:]
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response = tokenizer.batch_decode(
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response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)[0]
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print(f"response:{response}")
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"""
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ChatGPT的英文输出结果的逻辑性和准确性远大于中文结果的原因可能是因为以下几点：首先，ChatGPT的训练数据主要来自英文互联网上的大量文本，因此它在处理英文语言时具有更强的能力。其次，英文语言的语法和词汇结构相对简单，容易被ChatGPT理解和生成。此外，ChatGPT的算法优化也可能更适合处理英文语言。然而，对于中文语言，由于其复杂的语法结构和大量的非字节字符，ChatGPT可能难以完全理解和生成准确的中文结果。
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"""
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``` |