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Model: tommy1235/cvx-coder
Source: Original Platform

README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+
+---
+## 简介
+
+cvx-coder 增强了大模型[CVX](https://cvxr.com/cvx) 代码能力和QA能力。它是[phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)在CVX文档, 合成代码, 论坛对话数据上的微调版本。
+
+## 开始
+先下载模型：
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/tommy1235/cvx-coder.git
+```
+然后运行下面示例代码
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+m_path="你的路径/cvx-coder"
+model = AutoModelForCausalLM.from_pretrained(
+    m_path, 
+    device_map="auto", 
+    torch_dtype="auto", 
+    trust_remote_code=True, 
+)
+tokenizer = AutoTokenizer.from_pretrained(m_path)
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+generation_args = {
+    "max_new_tokens": 2000,
+    "return_full_text": False,
+    "temperature": 0,
+    "do_sample": False,
+}
+content='''my problem is not convex, can i use cvx? if not, what should i do, be specific.'''
+messages = [
+    {"role": "user", "content": content},
+]
+output = pipe(messages, **generation_args)
+print(output[0]['generated_text'])
+```
+若想进入**聊天模式**，请运行下面的代码：
+```python
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+m_path="你的路径/cvx-coder"
+model = AutoModelForCausalLM.from_pretrained(
+    m_path, 
+    device_map="auto", 
+    torch_dtype="auto", 
+    trust_remote_code=True, 
+)
+tokenizer = AutoTokenizer.from_pretrained(m_path)
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+generation_args = {
+    "max_new_tokens": 2000,
+    "return_full_text": False,
+    "temperature": 0,
+    "do_sample": False,
+}
+
+def assistant_talk(message, history):
+    message=[
+        {"role": "user", "content": message},
+        ]
+    temp=[]
+    for i in history:
+        temp+=[{"role": "user", "content": i[0]},{"role": "assistant", "content": i[1]}]
+        
+    messages =temp  + message
+
+    output = pipe(messages, **generation_args)
+    return output[0]['generated_text']
+gr.ChatInterface(assistant_talk).launch()
+```