File size: 3,680 Bytes
6f4536e cd6d469 6f4536e 6a12cd1 6f4536e 988bd06 6f4536e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
license: apache-2.0
datasets:
- BAAI/IndustryInstruction_Artificial-Intelligence
- BAAI/IndustryInstruction
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- 人工智能领域语言模型
- 机器学习领域语言模型
---
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)
## training params
The training framework is llama-factory, template=llama3
```
learning_rate=1e-5
lr_scheduler_type=cosine
max_length=2048
warmup_ratio=0.05
batch_size=64
epoch=10
```
select best ckpt by the evaluation loss
## evaluation
Duto to there is no evaluation benchmark, we can not eval the model
## How to use
```python
# !/usr/bin/env python
# -*- coding:utf-8 -*-
# ==================================================================
# [Author] : xiaofeng
# [Descriptions] :
# ==================================================================
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
llama3_jinja = """{% if messages[0]['role'] == 'system' %}
{% set offset = 1 %}
{% else %}
{% set offset = 0 %}
{% endif %}
{{ bos_token }}
{% for message in messages %}
{% if (message['role'] == 'user') != (loop.index0 % 2 == offset) %}
{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
{% endif %}
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|start_header_id|>' + 'assistant' + '<|end_header_id|>\n\n' }}
{% endif %}"""
dtype = torch.bfloat16
model_dir = "MonteXiaofeng/Artificial-llama3_1_8B_instruct"
model = AutoModelForCausalLM.from_pretrained(
model_dir,
device_map="cuda",
torch_dtype=dtype,
)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
tokenizer.chat_template = llama3_jinja # update template
message = [
{"role": "system", "content": "You are a helpful assistant"},
{
"role": "user",
"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？",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
print(prompt)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
prompt_length = len(inputs[0])
print(f"prompt_length:{prompt_length}")
generating_args = {
"do_sample": True,
"temperature": 1.0,
"top_p": 0.5,
"top_k": 15,
"max_new_tokens": 512,
}
generate_output = model.generate(input_ids=inputs.to(model.device), **generating_args)
response_ids = generate_output[:, prompt_length:]
response = tokenizer.batch_decode(
response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
print(f"response:{response}")
"""
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。
"""
``` |