Instructions to use Ninnix96/Qwengram-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Ninnix96/Qwengram-4B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ninnix96/Qwengram-4B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Ninnix96/Qwengram-4B:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Ninnix96/Qwengram-4B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Ninnix96/Qwengram-4B:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Ninnix96/Qwengram-4B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ninnix96/Qwengram-4B:Q4_K_M
Use Docker
docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Ninnix96/Qwengram-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ninnix96/Qwengram-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ninnix96/Qwengram-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
- Ollama
How to use Ninnix96/Qwengram-4B with Ollama:
ollama run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
- Unsloth Desktop
- Pi
How to use Ninnix96/Qwengram-4B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Ninnix96/Qwengram-4B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Ninnix96/Qwengram-4B with Docker Model Runner:
docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
- Lemonade
How to use Ninnix96/Qwengram-4B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ninnix96/Qwengram-4B:Q4_K_M
Run and chat with the model
lemonade run user.Qwengram-4B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Ninnix96/Qwengram-4B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Ninnix96/Qwengram-4B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Ninnix96/Qwengram-4B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Ninnix96/Qwengram-4B:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download runtime/results.json from Ninnix96/Qwengram-4B: direct link, hf CLI and curl.
- Browser
- Download file 24.2 kB
-
https://huggingface.co/Ninnix96/Qwengram-4B/resolve/main/runtime/results.json
- Command line
-
hf download hf://Ninnix96/Qwengram-4B/runtime/results.json
-
curl -L -o results.json https://huggingface.co/Ninnix96/Qwengram-4B/resolve/main/runtime/results.json
24.2 kB
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| "Q6_K": { | |
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| "perplexity_reduction_percent": { | |
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| }, | |
| "gain_difference_vs_bf16": { | |
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| "q4": 0.003662999999999972 | |
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| "gain_difference_95ci": { | |
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| "q4": [ | |
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| }, | |
| "chunk_nll": { | |
| "stock_bf16": [ | |
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| 2.786446000000005, | |
| 2.579727999999996, | |
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