Instructions to use alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql

SGLang

How to use alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql with Docker Model Runner:
```
docker model run hf.co/alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql
```

alpecevit commited on May 4, 2025

Commit

f377f47

verified ·

1 Parent(s): 4bf4bb0

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -3

README.md CHANGED Viewed

@@ -10,14 +10,17 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
-tokenizer = AutoTokenizer.from_pretrained("alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql")
 model = AutoModelForCausalLM.from_pretrained(
-    "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql",
     torch_dtype="auto",
     device_map="auto"
 )
-model = PeftModel.from_pretrained(model, LORA_WEIGHTS)
 model.eval()
 test_data = {

 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
+MODEL_NAME = "alpecevit/Qwen2.5-Coder-7B-Instruct-text2sql"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
     torch_dtype="auto",
     device_map="auto"
 )
+model = PeftModel.from_pretrained(model, MODEL_NAME)
 model.eval()
 test_data = {