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
- 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 Studio
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF to start chatting
- 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 @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": "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"
File size: 5,079 Bytes
0a95e53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | ---
license: apache-2.0
base_model:
- Qwen/Qwen3.8-27B
- ggml-org/Qwen3.8-27B-GGUF
base_model_relation: quantized
library_name: llama.cpp
pipeline_tag: text-generation
tags:
- gguf
- rocm
- rocmfpx
- rocmfp4
- amd
- strix-halo
- gfx1151
- mtp
- speculative-decoding
- qwen3.5
---
# Qwen3.8-27B β ROCmFP4 + MTP drafter ladder (Strix Halo)
ROCmFP4 builds of [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B), quantised from
`ggml-org/Qwen3.8-27B-GGUF`'s BF16 (sha256 `5a3eedc837bcbd13β¦`, verified), **plus an MTP drafter
at five precisions** so the speculative-decoding numbers below can be reproduced rather than
taken on trust.
Other ROCmFP4 builds of this model already exist. What is here and (at publication) not
elsewhere: **draft-acceptance rates**, a **per-backend `n-max` sweep**, a **`-ub` sweep**, and a
**ROCm 7.2 vs 10.1 comparison** that reverses the preset ranking.
> β **Read this before choosing FP4.** On the same machine, **mainline llama.cpp on Vulkan with a
> plain `Q4_K_M` and the official MTP drafter is faster** β 330 vs 227 prefill (+43%), decode tied.
> These FP4 files are worth it for the **1.7 GiB smaller footprint** (15.6 vs 17.3 GiB resident),
> which matters when co-residing two models. They are not the throughput winner.
## Files
| file | preset | size |
| --- | --- | ---: |
| `Qwen3.8-27B-ROCMFP4-STRIX.gguf` | `Q4_0_ROCMFP4_STRIX` | 13.75 GiB |
| `Qwen3.8-27B-ROCMFP4-COHERENT.gguf` | `Q4_0_ROCMFP4_COHERENT` | 14.41 GiB |
| `mtp-Qwen3.8-27B-ROCMFP4-STRIX.gguf` | FP4 drafter | 1.85 GiB |
| `mtp-Qwen3.8-27B-ROCMFP3.gguf` | FP3 drafter | 1.55 GiB |
| `mtp-Qwen3.8-27B-ROCMFP6.gguf` | FP6 drafter | 2.27 GiB |
| `mtp-Qwen3.8-27B-ROCMFP8.gguf` | FP8 drafter | 2.86 GiB |
| `mtp-Qwen3.8-27B-ROCMFP2.gguf` | FP2 drafter β **broken, see below** | 1.48 GiB |
Requires a [ROCmFPX](https://github.com/charlie12345/ROCmFPX) build; mainline llama.cpp does not
know the `Q4_0_ROCMFP4_*` tensor types.
## Hardware / method
AMD Ryzen AI MAX+ 395, Radeon 8060S (`gfx1151`, RDNA 3.5), 124 GiB GTT, Debian sid, kernel 7.2.
Server-measured (`llama-server` + probe), ~8K-token prompt, temperature 0, one job at a time.
ROCm nightly pinned to `therock-dist-linux-gfx1151-10.1.0a20260815`.
## 1. Draft depth (`--spec-draft-n-max`) is per-backend
| n-max | Vulkan Q4_K_M decode | acc | FP4 decode | acc |
| ---: | ---: | ---: | ---: | ---: |
| 3 | β | β | 33.42 | 100.0% |
| 4 | 35.80 | 88.1% | 35.16 | 98.7% |
| **5** | **38.94** | 91.6% | 38.67 | 98.1% |
| 6 | 38.47 | 86.5% | 38.04 | 97.5% |
| 7 | 37.84 | 82.0% | **39.26** | 94.7% |
| 8 | 28.56 | 78.1% | 32.36 | 95.2% |
| 10 | 25.47 | 59.4% | β | β |
Acceptance decays monotonically with depth; the knee is where verifying rejected drafts costs
more than the accepted ones save. FP4 holds higher acceptance, so its knee sits deeper and
flatter. **This is not DeepSeek-V4's n=2** β draft depth does not transfer between models.
## 2. Drafter precision is a bandwidth lever, not a quality one
Target fixed, drafter varied, Vulkan, n=5:
| drafter | size | decode | acceptance |
| --- | ---: | ---: | ---: |
| **Q4_K_M** | 1.89 GiB | **39.16** | 91.6% |
| Q6_K | 2.28 GiB | 38.28 | 92.1% |
| Q5_K_M | 2.08 GiB | 36.93 | 89.0% |
| Q8_0 | 2.95 GiB | 34.74 | 89.0% |
**Acceptance is flat (89β92%) while decode spans 13%** β so shrinking the drafter buys bandwidth
and costs nothing in draft quality. Advice to keep drafters at β₯Q8 does not hold here.
## 3. FP2 destroys a drafter
FPX ladder, STRIX target, ROCm 10.1, n=5:
| drafter | decode | acceptance |
| --- | ---: | ---: |
| FP4-STRIX | **37.03** | 97.4% |
| FP3 | 36.07 | **98.1%** |
| FP6 | 30.92 | 96.6% |
| FP8 | 29.76 | 96.6% |
| **FP2** | 22.07 | **64.0%** |
FP2's codebook has no exact zero. This drafter is BF16-sourced β the case usually assumed safe β
and acceptance still collapses. **Do not use FP2 for a draft model.**
## 4. The preset ranking flips with the ROCm version
`llama-bench`, pp2048:
| preset | ROCm 7.2.4 | ROCm 10.1 nightly |
| --- | ---: | ---: |
| COHERENT | **205.7** | 208.6 (+1%) |
| STRIX | 151.8 | **272.0 (+79%)** |
COHERENT wins on 7.2; STRIX wins on 10.1. Any "preset X is best" claim β including ones in other
repos β is conditional on a ROCm version that usually goes unstated.
## 5. `-ub 256`, not the default
| `-ub` | Vulkan pp2048 | FP4 pp2048 (10.1) |
| ---: | ---: | ---: |
| **256** | **370.6** | **300.3** |
| 512 | 360.9 | 267.3 |
| 1024 | 343.7 | 236.5 |
| 2048 | 332.0 | 235.8 |
Both backends prefer a small micro-batch; `-ub 2048` costs FP4 **27%** of its prefill. Batch size
(`-b` 512β4096) changes nothing.
## Usage
```bash
llama-server \
-m Qwen3.8-27B-ROCMFP4-STRIX.gguf \
-md mtp-Qwen3.8-27B-ROCMFP4-STRIX.gguf \
--spec-type draft-mtp --spec-draft-n-max 5 \
-ngl 99 -ngld 99 -fa on -ub 256
```
## Provenance
Every file derives from `ggml-org/Qwen3.8-27B-GGUF`, sha256-verified before quantisation:
`Qwen3.8-27B-BF16.gguf` = `5a3eedc837bcbd13β¦`, `mtp-Qwen3.8-27B-BF16.gguf` = `5723e551c4ee2b8cβ¦`.
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