Instructions to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF 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 gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF 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 gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
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 gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: ./llama-cli -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
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 gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: ./build/bin/llama-cli -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
Use Docker
docker model run hf.co/gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
- LM Studio
- Jan
- vLLM
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
- Ollama
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Ollama:
ollama run hf.co/gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
- Unsloth Studio
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF to start chatting
- Pi
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
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 gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
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 "gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP" \ --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"
- Docker Model Runner
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Docker Model Runner:
docker model run hf.co/gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
- Lemonade
How to use gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gsrunion/gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
Run and chat with the model
lemonade run user.gpt-oss-120b-ROCmFP4-STRIX_LEAN-GGUF-Q4_0_ROCMFP
List all available models
lemonade list
GPT-OSS-120B β ROCmFP4 STRIX_LEAN for AMD Strix Halo
ROCmFP4 GGUF requantization of openai/gpt-oss-120b (117B MoE, 5.1B active), produced for AMD Strix Halo (Ryzen AI Max, gfx1151).
To our knowledge the first ROCmFP4-family quant of gpt-oss. +21% decode over the native MXFP4 release on Strix Halo at essentially identical size β the gain is pure kernel efficiency (the ROCmFP4 layout streams at near-peak bandwidth on gfx1151).
β οΈ Stock llama.cpp will reject this file (
invalid ggml type 106). It runs on:
- the kyuz0/amd-strix-halo-toolboxes
rocm-7.2.4-rocmfp4container image, or- the Hal0ai/Hal0_ROCmFPX llama.cpp fork.
Companion repos: Qwen3.6-35B-A3B Β· Nemotron-3-Nano-30B Β· Ornith-1.0-35B Β· Ornith-1.0-9B
Files
| File | Quant | BPW | Size |
|---|---|---|---|
gpt-oss-120b-Q4_0_ROCMFP4_STRIX_LEAN.gguf |
Q4_0_ROCMFP4_STRIX_LEAN | ~4.25 | 62.4 GB |
Measured performance
AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified LPDDR5X, ROCm, llama-bench -fa 1 --mmap 0):
| Quant | Size | pp512 | tg128 |
|---|---|---|---|
| Q4_0_ROCMFP4_STRIX_LEAN | 58.1 GiB | 593 | 61.7 |
| native MXFP4 (ggml-org) | 59.0 GiB | 587 | 50.9 |
Provenance & quality disclosure
Requantized from the native MXFP4 release (no BF16 master exists for gpt-oss) with llama-quantize --allow-requantize, calibrated with bartowski's imatrix. FP4βFP4 regridding adds rounding noise; on an 8-prompt quality battery vs the native release (temp 0), 7/8 answers were byte-equivalent-or-equal-quality and one contained a minor arithmetic slip in an illustrative example (the substantive answer remained correct). Judge that trade for your workload β the native MXFP4 remains available from ggml-org.
Serving
llama-server -m gpt-oss-120b-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
-ngl 999 -fa on --jinja -c 65536
Credits
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Base model
openai/gpt-oss-120b