Instructions to use drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF
- SGLang
How to use drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF 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 "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF" \ --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": "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF", "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 "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF" \ --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": "drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF
drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF
This model was converted to GGUF format from Qwen/Qwen3-4B-Instruct-2507 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-4b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-4b-instruct-2507-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-4b-instruct-2507-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF --hf-file qwen3-4b-instruct-2507-q8_0.gguf -c 2048
- Downloads last month
- 25
8-bit
Model tree for drmcbride/Qwen3-4B-Instruct-2507-Q8_0-GGUF
Base model
Qwen/Qwen3-4B-Instruct-2507