Text Generation
GGUF
llama.cpp
rocm
rocmfpx
rocmfp4
amd
strix-halo
gfx1151
mtp
speculative-decoding
qwen3.5
conversational
Instructions to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF # Run inference directly in the terminal: llama cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF # Run inference directly in the terminal: llama cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF # Run inference directly in the terminal: ./llama-cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
Use Docker
docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
- Llama new
- LM Studio
- Jan
- vLLM
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "singulared/Qwen3.8-27B-ROCmFP4-MTP-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": "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
- Ollama
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Ollama:
ollama run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
- Unsloth Desktop
- Pi
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
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": "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Docker Model Runner:
docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
- Lemonade
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
Run and chat with the model
lemonade run user.Qwen3.8-27B-ROCmFP4-MTP-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
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 "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF" \ --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"
Commit History
-
Correct KV recommendation: -ctv turbo4 corrupts long-context output
Verified signatureThe Usage block recommended -ctk q8_0 -ctv turbo4 as a free memory saving. That pairing silently corrupts retrieval past ~10K tokens while short prompts stay perfect. Corrected to -ctk q8_0 -ctv q8_0. Isolated with a needle test at ~14.6K tokens, FP4_FAST, no draft model, only the KV types varying: f16 / f16 PASS q8_0 / turbo4 FAIL q8_0 / q8_0 PASS f16 / turbo4 PASS Only the pairing fails -- each component alone is correct. Both turbo4 configs log 'attention rotation disabled', including the passing one, so that startup warning is not diagnostic. It hides by inflating throughput: the corrupted output is repetitive, so the draft head accepts nearly all of it (acceptance 0.92-0.98 against a normal 0.64-0.77) and decode reads about 40 percent high. The 8K decode figure in section 0 is therefore corrected from 38-42 t/s at 0.92-0.98 to ~28 t/s at 0.64-0.77, re-measured with correct KV, one model resident, 4 reps, median. New section 6 documents the trap in full.02448e6 -
Add measured perplexity for all three presets; document the FAST files
Verified signatureNew section 4: perplexity on held-out wikitext-2, 145 chunks at ctx 2048, identical chunks for every model, measured on this machine. Q4_K_M (reference) 6.3383 +/- 0.0402 15.41 GiB ROCMFP4-STRIX 6.3715 +/- 0.0402 13.75 GiB ROCMFP4-FAST 6.3968 +/- 0.0404 13.33 GiB ROCMFP4-COHERENT 6.5002 +/- 0.0417 14.41 GiB - COHERENT is dominated by STRIX: larger AND clearly worse (+0.129, about three times the error bar). Its only advantage is prefill on ROCm 7.2, which is version-conditional. Do not pick it for quality. - STRIX vs FAST is +0.025 -- below either error bar, but the two run on identical chunks and STRIX is lower at every cumulative checkpoint from 1 to 145, so the ordering is systematic rather than noise. FAST costs ~0.4% perplexity and saves 0.42 GiB; both are defensible. - Q4_K_M has the lowest perplexity of all four. FP4 trades ~0.5% perplexity for ~13% less memory -- it is not free. The FAST model and its matching FP4 drafter are now …36f26de -
Add FAST preset: model + matching FP4 drafter
Verified signatureQ4_0_ROCMFP4_FAST (ftype 103, 4.25 bpw) plus its matching FAST-preset MTP drafter. This is the pair we actually serve in production, previously referenced in the card but absent from the repo. Smallest of the three presets at 13.33 GiB (vs STRIX 13.75, COHERENT 14.41). Speed measures equivalent to STRIX, so the tiebreak between them is perplexity.3081951 -
Rewrite card: decode is acceptance-dominated, so it is task-dependent
Verified signatureRestructured as a single current-state document. - New section 0: decode tracks draft acceptance almost linearly, so it is task-dependent -- 0.92-0.98 acceptance / 38-42 t/s summarizing an 8K doc vs 0.60-0.66 / 27-28 t/s on a short open-ended prompt. Same files, same flags. All other tables are now explicitly framed as the high-acceptance regime. - Positioning corrected up front: with MTP enabled on both sides, mainline Q4_K_M on Vulkan TIES on decode (38.94 vs 38.67) and WINS prefill (330 vs 227). FP4's win is footprint (1.7 GiB less resident), not throughput. - n-max: added an independent server re-sweep (n=4 39.3, n=6 38.5, n=3 35.5 -- the top of the curve is broad) and the warning that llama.cpp's default of 16 roughly halves throughput. - Drafter: keep it as a separate file. A grafted/embedded MTP head measured 23.0 t/s vs 25.7 for the same model with a sidecar drafter. - ROCm version: the preset table is prefill-only and decode moves the other way. The 10.1 nig…562c67e
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