Instructions to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./llama-cli -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
Use Docker
docker model run hf.co/kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
- LM Studio
- Jan
- vLLM
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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": "kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
- Ollama
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with Ollama:
ollama run hf.co/kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
- Unsloth Studio
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF to start chatting
- Pi
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
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": "kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with Docker Model Runner:
docker model run hf.co/kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
- Lemonade
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
Run and chat with the model
lemonade run user.Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF-Q8_0_ROCMFPX
List all available models
lemonade list
- Hermes Agent
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
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 kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX
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 "kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF:Q8_0_ROCMFPX" \ --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"
⚠️ STOCK
llama.cppWILL NOT LOAD THIS MODEL
Q8_0_ROCMFPX_AGENTis a ROCmFPX quant type — it exists only incharlie12345/ROCmFPX, not upstream llama.cpp.📦 26.28 GiB, 8.39 bpw · ✅ tools 7/7 both modes 🚀 26.62 tok/s with MTP — 6.2% faster than the plain 8-bit build, because it accepts more draft tokens (0.953 vs 0.911). ⚠️ Without MTP the two are indistinguishable.
Qwen3.8-27B — ROCmFPX 8-bit AGENT routing (Q8_0_ROCMFPX_AGENT) GGUF
The agent / tool-call coherent 8-bit ROCmFPX routing of Qwen3.8-27B for AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo). Quantized from the 51.3 GiB BF16 GGUF.
| File | Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf |
| Size | 26.2767 GiB (28,214,425,952 bytes) |
| BPW | 8.39 |
| ftype | Q8_0_ROCMFPX_AGENT (115) |
| sha256 | f3ef2bdbf56e244fff14b72114dc2aa300b84a4cb4eaf54f2e5e4da1cfcae10b |
⚖️ Why this variant exists — and when it does NOT
The routing difference is structural. Across the 17-tensor *output.weight group:
| build | Q8_0 | ROCmFPX 8-bit (TYPE_103) |
|---|---|---|
plain Q8_0_ROCMFPX |
1 | 16 |
this (_AGENT) |
13 | 4 |
AGENT keeps 12 more attention-output projections at full Q8_0 — that is what the extra 0.35 GiB buys.
With MTP, that buys real speed
Ryzen AI MAX+ 395 (gfx1151, ROCm 7.2.4), median of 3, warm-up discarded, idle box, shipped
flags (--spec-type draft-mtp, Q4_0 draft head, --spec-draft-n-max 4):
| build | size | decode WITH MTP | range | draft acceptance |
|---|---|---|---|---|
plain Q8_0_ROCMFPX |
25.92 GiB | 25.07 tok/s | [25.07 – 25.51] | 0.911 |
this _AGENT |
26.28 GiB | 26.62 tok/s | [26.61 – 27.15] | 0.953 |
Q4_0_ROCMFP4_STRIX (4-bit) |
14 GiB | 38.32 tok/s | [37.91 – 38.61] | 1.000 |
+6.2% over plain, ranges disjoint. The mechanism is visible in the acceptance rate: keeping more attention-output precision makes the MTP draft head's guesses land more often, so more speculated tokens survive verification.
⚠️ Without MTP there is no measurable difference
Run standalone, the two builds are 7.92 vs 7.82 tok/s (this one marginally slower), and both score 7/7 tool calling in thinking and non-thinking — identical across multi-arg, nested-object, enum, declines, multi-turn, streaming and parallel calls. Our 7-case suite cannot separate them.
⇒ Take this variant if you serve with MTP. Take the plain build if you do not. And if you want raw throughput, the 4-bit build beats both by ~1.44×.
llama-server -m Qwen3.8-27B-Q8_0_ROCMFPX_AGENT.gguf \
--spec-type draft-mtp --model-draft mtp-Qwen3.8-27B-Q4_0.gguf \
--spec-draft-ngl 99 --spec-draft-n-max 4 \
-ngl 999 -fa on -fit off --jinja --ctx-size 32768
Verified
17×23 ⇒ ✅ 391 · capital of Japan ⇒ ✅ Tokyo · days in 2024 ⇒ ✅ 366 ·
tools 7/7 thinking, 7/7 non-thinking.
token_embd.weight Q8_0, 851 tensors, arch=qwen35.
🩹 Prompt caching with the MTP draft head — fixed
Reported by a user of this repo: with --spec-type draft-mtp loaded, llama-server disabled
prefix caching entirely. Every agentic turn reprocessed the whole prompt. Reproduced here on an
8045-token stable prefix:
| config | prompt_n | cache_n | prompt_ms |
|---|---|---|---|
| no draft head | 519 | 7526 reused | 1 908 |
| draft head (the defect) | 8045 | 0 | 27 948 |
| draft head + this patch | 4 | 5101 | 100 |
279× less prompt processing per turn, with MTP still drafting.
Root cause
The saved speculative state is the MTP boundary — the target model's pre-norm hidden row at the cached prompt's exact end position. Any partial-prefix reuse would leave it describing a position that no longer exists, so the server demanded an exact full-prefix match and otherwise reprocessed cold, erasing its own context checkpoints on the way.
The fix
patches/mtp-prompt-cache-fix.patch (4 files, applies to 2809dc5) captures the speculative
boundary inside the context checkpoint (common_prompt_checkpoint::data_spec).
create_checkpoint runs between decode batches — exactly where that boundary is valid — so exact
state is saved and restored together with the KV, never rebuilt.
⛔ Two approaches were tried first and rejected: rebuilding the boundary from a zero-fill changed the model's output (deterministically, 3/3), and truncating the KV back to the reuse point is impossible here — the bounded rollback window is 4 tokens against the 333 a real turn needs. Exact state restore is the only shape that preserves output.
Verification
Independently gated 10/10: same prompt cold vs warm, temperature 0, byte-identical every run,
with the cache genuinely engaged (cache_n=5101, not a vacuous pass). The output hash also matches
the unpatched build, so behaviour is unchanged. Fails closed — an unreachable rollback logs
reason=spec-checkpoint-missing and cold-reprocesses rather than guessing.
Related upstream
This is the same family as open llama.cpp issues #20225, #19794 and #24055 — checkpoints being invalidated on hybrid/recurrent models. This patch is not upstreamed; it is offered here as-is.
What was NOT measured
- No perplexity, no quality A/B vs BF16 or the plain 8-bit build. We have shown a structural difference in tensor types and no measurable behavioural difference.
- No long-context testing (131,072 supported), no coding/reasoning benchmark.
- Our tool suite is 7 cases. A 7/7 tie is not proof of equivalence — it is the limit of what this instrument can resolve. The MTP acceptance rate did separate the builds where the tool suite could not.
- An earlier revision of this card said no advantage over the plain build could be measured. That was true without MTP and wrong with it; the table above supersedes it.
Base model licence inherited. Credit for the model goes to Qwen.
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