Update README.md

README.md

@@ -14,6 +14,131 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+# Install some dependency 
+```bash
+pip install openai huggingface_hub
+```
+
+# Download lora 
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+ repo_id="svjack/Qwen2-7B_Function_Call_tiny_lora",
+ repo_type="model",
+ local_dir="Qwen2-7B_Function_Call_tiny_lora",
+ local_dir_use_symlinks = False
+ )
+```
+
+# Start OpenAI style api server 
+```bash
+python src/api.py \
+--model_name_or_path Qwen/Qwen2-7B-Instruct \
+--template qwen \
+--adapter_name_or_path Qwen2-7B_Function_Call_tiny_lora \
+--quantization_bit 4
+```
+
+# Inference 
+```python
+import json
+import os
+from typing import Sequence
+
+from openai import OpenAI
+from transformers.utils.versions import require_version
+
+require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
+
+def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
+ grade_to_score = {"A": 4, "B": 3, "C": 2}
+ total_score, total_hour = 0, 0
+ for grade, hour in zip(grades, hours):
+ total_score += grade_to_score[grade] * hour
+ total_hour += hour
+ return round(total_score / total_hour, 2)
+
+client = OpenAI(
+ api_key="0",
+ base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
+ )
+
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "calculate_gpa",
+ "description": "Calculate the Grade Point Average (GPA) based on grades and credit hours",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "grades": {"type": "array", "items": {"type": "string"}, "description": "The grades"},
+ "hours": {"type": "array", "items": {"type": "integer"}, "description": "The credit hours"},
+ },
+ "required": ["grades", "hours"],
+ },
+ },
+ }
+ ]
+tool_map = {"calculate_gpa": calculate_gpa}
+
+messages = []
+messages.append({"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."})
+
+result = client.chat.completions.create(messages=messages,
+ model="Qwen/Qwen2-7B-Instruct", tools=tools)
+
+result.choices[0].message.tool_calls
+
+messages.append(result.choices[0].message)
+tool_call = result.choices[0].message.tool_calls[0].function
+print(tool_call)
+
+name, arguments = tool_call.name, json.loads(tool_call.arguments)
+tool_result = tool_map[name](**arguments)
+
+messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
+
+result = client.chat.completions.create(messages=messages, model="test", tools=tools)
+print(result.choices[0].message.content)
+```
+
+# Output 
+```
+Function(arguments='{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}', name='calculate_gpa')
+Based on the grades and credit hours you provided, your calculated GPA is 3.42.
+```
+
+# Inference 
+```python
+messages = []
+messages.append({"role": "user", "content": "我的成绩分别是A,A,B,C学分分别是3, 4, 3,和2"})
+
+result = client.chat.completions.create(messages=messages,
+ model="Qwen/Qwen2-7B-Instruct", tools=tools)
+
+result.choices[0].message.tool_calls
+
+messages.append(result.choices[0].message)
+tool_call = result.choices[0].message.tool_calls[0].function
+print(tool_call)
+
+name, arguments = tool_call.name, json.loads(tool_call.arguments)
+tool_result = tool_map[name](**arguments)
+
+messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
+
+result = client.chat.completions.create(messages=messages, model="test", tools=tools)
+print(result.choices[0].message.content)
+```
+
+# Output 
+```
+Function(arguments='{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}', name='calculate_gpa')
+您提供的成绩和学分的加权平均分（GPA）是3.42。
+```
+
+
 # train_2024-06-17-19-49-05
 
 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the glaive_toolcall_zh and the glaive_toolcall_en datasets.