How to use from
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 "Bin12345/AutoCoder_QW_7B" \
    --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": "Bin12345/AutoCoder_QW_7B",
		"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 "Bin12345/AutoCoder_QW_7B" \
        --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": "Bin12345/AutoCoder_QW_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

The base model of AutoCode_QW_7B is CodeQwen1.5-7b.

In this version, we fixed the problem that the model will only start the code interpreter when you ask it to verify its code.

you can try the code interpreter function on the AutoCoder GitHub

For the simple code generation without code interpreter ability, try the following script:

from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset
model_path = "Bin12345/AutoCoder_QW_7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, 
                                             device_map="auto")

Input = "" # input your question here
 
messages=[
    { 'role': 'user', 'content': Input}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, 
                                        return_tensors="pt").to(model.device)
outputs = model.generate(inputs, 
                        max_new_tokens=1024, 
                        do_sample=False, 
                        temperature=0.0,
                        top_p=1.0, 
                        num_return_sequences=1, 
                        eos_token_id=tokenizer.eos_token_id)
answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
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