Create README.md

README.md

@@ -0,0 +1,114 @@
+---
+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
+"""
+
+```