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Model: jinvbar/hebei-tourism-deepseek
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ModelHub XC
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*.safetensors filter=lfs diff=lfs merge=lfs -text

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README.md Normal file
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---
license: apache-2.0
tags:
- transformers
- safetensors
- llama
- hebei-tourism
- deepseek
tasks:
- text-generation
- hebei-tourism
- deepseek
widgets:
- enable: true
version: 1
task: text-generation
inputs:
- type: text
displayType: TextArea
name: input_text
validator:
max_words: 128
displayProps:
label: 输入文本
placeholder: 请输入要处理的文本,例如‘推荐河北的旅游景点’
output:
displayType: Text
displayValueMapping: text
examples:
- inputs:
- data: 推荐河北的旅游景点
- inputs:
- data: 河北有哪些历史文化名城
---
# 河北旅游DeepSeek 模型
这是一个基于 `deepseek-ai/deepseek-coder-6.7b-base` 微调的文本生成模型,专门用于推荐河北旅游相关的景点、文化名城等。
## 模型用途
- **模型框架**`transformers` 库,支持文本生成。
- **支持任务**:文本生成(`text-generation`)。
## 使用方法
你可以使用 `transformers` 库加载模型:
```python
from modelscope import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("jinvbar/hebei-tourism-deepseek")
tokenizer = AutoTokenizer.from_pretrained("jinvbar/hebei-tourism-deepseek")
input_text = "介绍一下河北的旅游景点"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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{
"library": "transformers",
"pipeline_tag": "text-generation",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 32013,
"eos_token_id": 32014,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 5504,
"max_position_embeddings": 16384,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"num_key_value_heads": 16,
"pretraining_tp": 1,
"rms_norm_eps": 0.000001,
"rope_scaling": {
"factor": 4.0,
"rope_type": "linear",
"type": "linear"
},
"rope_theta": 100000,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.50.1",
"use_cache": true,
"vocab_size": 32256
}

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generate_readme.py Normal file
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import os
定义 README.md 的内容
readme_content = """---
library: transformers
pipeline_tag: text-generation
tags:
- hebei-tourism
- deepseek
- llama
Hebei Tourism Deepseek Model
This is a fine-tuned model based on deepseek-ai/deepseek-coder-1.3b-base for providing information about tourism in Hebei, China. The model is fine-tuned using the transformers library and supports text generation tasks.
Usage
You can use this model with the transformers library:
python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("jinv2/hebei-tourism-deepseek")
tokenizer = AutoTokenizer.from_pretrained("jinv2/hebei-tourism-deepseek")
input_text = "介绍一下河北的旅游景点"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
"""
定义保存路径
save_path = "/home/mmm/桌面/deepseek/hebei_model_merged/README.md"
写入 README.md 文件
try:
with open(save_path, "w", encoding="utf-8") as f:
f.write(readme_content)
print(f"Successfully generated README.md at {save_path}")
except Exception as e:
print(f"Failed to generate README.md: {e}")
验证文件内容
if os.path.exists(save_path):
with open(save_path, "r", encoding="utf-8") as f:
print("\nGenerated README.md content:")
print(f.read())

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{
"_from_model_config": true,
"bos_token_id": 32013,
"eos_token_id": 32014,
"transformers_version": "4.50.1"
}

Submodule hebei-tourism-deepseek added at 3d16479cdd

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inference.py Normal file
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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# 加载模型和分词器
model_path = "path/to/your/model" # ModelScope 会自动设置路径
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
def inference(input_text):
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# 示例输入
if __name__ == "__main__":
input_text = "河北旅游推荐"
result = inference(input_text)
print(result)

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}

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model_config.json Normal file
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{
"task": "text-generation",
"model": {
"type": "transformers",
"path": "."
},
"inference": {
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"parameters": [
{
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"required": true
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}
},
"dependencies": {
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},
"resources": {
"instance_type": "GPU-16GB"
}
}

2
requirements.txt Normal file
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transformers
torch

23
special_tokens_map.json Normal file
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{
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"rstrip": false,
"single_word": false
}
}

159358
tokenizer.json Normal file

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194
tokenizer_config.json Normal file
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