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Automobile-llama3_1_8B_instruct

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Automobile-llama3_1_8B_instruct
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---
license: apache-2.0
datasets:
- BAAI/IndustryInstruction_Automobiles
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
---

This model is finetuned on the model llama3.1-8b-instruct using the dataset [BAAI/IndustryInstruction_Automobiles](https://huggingface.co/datasets/BAAI/IndustryInstruction_Automobiles) 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/Automobile-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": "随着特斯拉和小米汽车等新势力的崛起，传统车企如何应对互联网和科技公司的挑战，加速向智能化、电动化的方向转型？",
    },
]
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}")
"""
传统车企应积极拥抱互联网和科技公司的挑战，加速向智能化、电动化的方向转型。首先，车企需要加强与科技公司的合作，利用其在人工智能、自动驾驶等领域的技术优势，提升自身产品的智能化水平。其次，车企应加大在电动化领域的投入，研发更多电动车型，满足市场对环保、节能的需求。同时，车企还应加强与电池供应商的合作，提升电动车的续航里程和充电速度，提高用户体验。此外，车企还应加强在智能互联方面的投入，提供更好的车联网服务，满足用户对智能化、便捷化的需求。总之，传统车企应积极应对互联网和科技公司的挑战，加速向智能化、电动化的方向转型，以适应市场的变化，保持竞争力
"""

```