Instructions to use tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3 with Transformers:

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

pipe = pipeline("text-generation", model="tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3")
model = AutoModelForCausalLM.from_pretrained("tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3

SGLang

How to use tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3 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 "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3 with Docker Model Runner:
```
docker model run hf.co/tchubakov/Qwen2.5-Coder-7B-Instruct-gptq-v3
```

Qwen2.5-Coder-7B-Instruct-gptq-v3

File size: 265 Bytes

3f71637

{
  "bos_token_id": 151643,
  "do_sample": true,
  "eos_token_id": [
    151645,
    151643
  ],
  "max_length": 32768,
  "pad_token_id": 151665,
  "repetition_penalty": 1.1,
  "temperature": 0.7,
  "top_k": 20,
  "top_p": 0.8,
  "transformers_version": "4.47.1"
}