Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- BAAI/IndustryInstruction
+- BAAI/IndustryInstruction_Technology-Research
+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_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/Technology-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": "请详细描述科技研究如何改变了我们的教育系统。",
+    },
+]
+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]
+
+
+"""
+技研究对我们的教育系统产生了深远的影响。首先，科技研究使得教育变得更加普及。通过互联网和数字化技术，学生可以在任何时间、任何地点接受教育，这大大增加了教育的可获取性。其次，科技研究也使得教育变得更加个性化。通过大数据和人工智能等技术，教育系统可以根据每个学生的学习情况和需求，提供定制化的教学方案。此外，科技研究还促进了教育的互动性。通过虚拟现实、增强现实等技术，学生可以更好地参与到学习中来，提高学习的趣味性和效果。总的来说，科技研究正在不断地推动教育系统的发展，使教育更加普及、个性化和互动。
+"""
+print(f"response:{response}")
+
+
+```