Instructions to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX 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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./llama-cli -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

Use Docker

docker model run hf.co/JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

LM Studio
Jan

Ollama

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with Ollama:

ollama run hf.co/JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

Unsloth Studio

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX 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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX 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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JackBinary/Qwen3.8-27B-GGUF-ROCMFPX to start chatting

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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": "JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with Docker Model Runner:
```
docker model run hf.co/JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX
```

Lemonade

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

Run and chat with the model

lemonade run user.Qwen3.8-27B-GGUF-ROCMFPX-Q8_0_ROCMFPX

List all available models

lemonade list

Hermes Agent

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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 JackBinary/Qwen3.8-27B-GGUF-ROCMFPX:Q8_0_ROCMFPX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JackBinary/Qwen3.8-27B-GGUF-ROCMFPX with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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 "JackBinary/Qwen3.8-27B-GGUF-ROCMFPX: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"

Qwen3.8-27B-GGUF-ROCMFPX / README.md

JackBinary

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.8-27B
	base_model_relation: quantized
	tags:
	- gguf
	- rocmfpx
	- qwen3_5
	quantized_from: unsloth/Qwen3.8-27B-GGUF (BF16)
	---

	# Qwen3.8-27B — ROCmFPX quants (Q8 full + 16 GB hybrid)

	These are quantizations of [`unsloth/Qwen3.8-27B-GGUF` (BF16)](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF)
	(original model: [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B), the hybrid
	attention/SSM `qwen35` architecture with an MTP `nextn` head).

	> [!IMPORTANT]
	> You need the [ROCmFPX fork of llama.cpp](https://github.com/charlie12345/ROCmFPX) (or a llama.cpp
	> build with ROCmFPX support). These files use the experimental `q8_0_rocmfpx` (type 103) and/or
	> `q4_0_rocmfp4_fast` (type 101) weight formats, which stock llama.cpp releases do not
	> understand — loading them elsewhere will fail with an unknown tensor type error.

	## Files

	\| File \| Recipe \| Size \| For \|
	\|---\|---\|---\|---\|
	\| `Qwen3.8-27B-Q8_0_ROCMFPX.gguf` \| pure `q8_0_rocmfpx`, all weights \| 26.9 GB (8.25 bpw) \| large-VRAM systems (e.g. Strix Halo) \|
	\| `Qwen3.8-27B-Q4FAST-Q8-sensitive.gguf` \| bulk `q4_0_rocmfp4_fast` + sensitive tensors at `q8_0_rocmfpx` \| 16.4 GB (5.15 bpw) \| ~24 GB VRAM laptops (leaves room for KV cache) \|

	### Hybrid recipe (16 GB file)

	Sensitive-tensor selection mirrors the tiers in Unsloth's
	[`UD-Q4_K_XL`](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF) dynamic recipe (Q6_K tier =
	most sensitive, Q5_K = next), rebalanced onto a strict two-type q4/q8 mix to hit ~16 GB:

	\| Tensors at `q8_0_rocmfpx` (165 tensors) \| Why \|
	\|---\|---\|
	\| `attn_q/k/v/output` (17 full-attention layers) \| attention projections (Unsloth Q5_K/Q6_K tier; `attn_v` is Q6_K there) \|
	\| `attn_gate` + `ssm_out` (48 linear-attn/SSM layers) \| Q5_K tier in UD-Q4_K_XL \|
	\| `output.weight` head \| Q6_K tier in UD-Q4_K_XL \|

	Everything else is `q4_0_rocmfp4_fast` (340 tensors, incl. `ffn_gate/up/down`, `attn_qkv`,
	`token_embd` — Unsloth keeps embeddings at Q4_K too), norms/biases stay `f32` (360 tensors).
	The MTP head (`nextn.eh_proj`) is auto-protected at `q8_0` by the quantizer's draft-sensitive logic.

	### How they were made

	```bash
	# pure Q8 (from the ROCmFPX fork; CPU-only build works fine for quantization)
	llama-quantize Qwen3.8-27B-BF16-00001-of-00002.gguf \
	Qwen3.8-27B-Q8_0_ROCMFPX.gguf Q8_0_ROCMFPX

	# 16 GB hybrid
	llama-quantize \
	--tensor-type "attn_q.weight=q8_0_rocmfpx" \
	--tensor-type "attn_k.weight=q8_0_rocmfpx" \
	--tensor-type "attn_v.weight=q8_0_rocmfpx" \
	--tensor-type "attn_output.weight=q8_0_rocmfpx" \
	--tensor-type "attn_gate.weight=q8_0_rocmfpx" \
	--tensor-type "ssm_out.weight=q8_0_rocmfpx" \
	--tensor-type "^output.weight=q8_0_rocmfpx" \
	Qwen3.8-27B-BF16-00001-of-00002.gguf \
	Qwen3.8-27B-Q4FAST-Q8-sensitive.gguf Q4_0_ROCMFP4_FAST
	```

	## Usage

	This quant is text weights only. Qwen3.8 is multimodal — for vision support, pair it with the
	mmproj file: this repo includes `mmproj-F16.gguf` (mirrored from
	[`unsloth/Qwen3.8-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF), which also offers
	`mmproj-BF16.gguf`).

	```bash
	# build ROCmFPX for your GPU (see the repo README; e.g. Strix Halo):
	env JOBS=16 scripts/build-strix-rocmfp4-mtp.sh

	./build-strix-rocmfp4/bin/llama-cli \
	-m Qwen3.8-27B-Q8_0_ROCMFPX.gguf --mmproj mmproj-F16.gguf \
	-dev Vulkan0 -ngl 999 -fa on --jinja
	```

	## Benchmarks

	> [!NOTE]
	> Benchmarks are pending — placeholder tables below.

	\| Backend / GPU \| File \| Prompt (tok/s) \| Generation (tok/s) \| Context \| Notes \|
	\|---\|---\|---\|---\|---\|---\|
	\| TBD (Strix Halo) \| Q8_0_ROCMFPX \| TBD \| TBD \| TBD \| TBD \|
	\| TBD (24 GB laptop) \| Q4FAST-Q8-sensitive \| TBD \| TBD \| TBD \| TBD \|

	Quality comparison vs BF16 source (perplexity / KLD): TBD.

	## Attribution & license

	- Quantized from: [`unsloth/Qwen3.8-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF) (BF16 shards);
	sensitivity tiers referenced from their `UD-Q4_K_XL`
	- Original model: [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B)
	- License: Apache-2.0 (inherited)
	- Quant formats by the [ROCmFPX project](https://github.com/charlie12345/ROCmFPX)