Instructions to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
# Run inference directly in the terminal:
llama cli -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
# Run inference directly in the terminal:
llama cli -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
# Run inference directly in the terminal:
./llama-cli -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

Use Docker

docker model run hf.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

LM Studio
Jan

vLLM

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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": "vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

Ollama
How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with Ollama:
```
ollama run hf.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
```

Unsloth Studio

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF to start chatting

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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": "vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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 "vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-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"

Docker Model Runner
How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF
```

Lemonade

How to use vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF / README.md

vmlinux

Update README.md

8e7ccaa verified 6 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-122B-A10B
	base_model_relation: quantized
	library_name: gguf
	pipeline_tag: text-generation
	tags:
	- gguf
	- qwen3.5
	- qwen3.5-moe
	- rocmfp4
	- imatrix
	- amd
	- vulkan
	- rocm
	- mtp
	- speculative-decoding
	- experimental
	---

	<div align="center">

	# Qwen3.5 122B-A10B · ROCmFP4 iMatrix

	### The official Qwen checkpoint in a compact, importance-calibrated ROCmFP4 GGUF

	122B total · 10B active · 60.70 GiB · 28.50 tok/s MTP-off · BF16 KLD 0.041366 · Decode 28.505
	Decode speed + 36.89% faster
	Size - 13.47gb smaller

	</div>

	> [!IMPORTANT]
	> This GGUF uses custom ROCmFP4 tensor types. It requires
	> [ROCmFPX](https://github.com/charlie12345/ROCmFPX) or a runtime with equivalent
	> support. Stock `llama.cpp`, Ollama, LM Studio, and similar stock runtimes
	> cannot load it.

	I also suggest usign the template from https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates Froggeric is tha dude.

	This release was tested only on AMD Strix Halo / `gfx1151`. Other AMD targets
	may require a different ROCmFPX build and are untested; CPU-only, NVIDIA, and
	non-ROCmFPX runtimes are not supported by this card.

	## Downloads

	\| Artifact \| Direct download \|
	\|---\|---\|
	\| Main iMatrix model — 60.70 GiB \| [Download `Qwen3.5-122B-A10B-ROCmFP4-iMatrix.gguf`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-ROCmFP4-iMatrix.gguf?download=true) \|
	\| Optional MTP companion — 2.14 GiB \| [Download `Qwen3.5-122B-A10B-ROCmFP4-MTP.gguf`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-ROCmFP4-MTP.gguf?download=true) \|
	\| Calibration matrix — 342.28 MiB \| [Download `Qwen3.5-122B-A10B-Bartowski.imatrix`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-Bartowski.imatrix?download=true) \|

	[Browse every repository file →](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/tree/main)

	## What it is

	This is an iMatrix-calibrated quantization of the official
	[`Qwen/Qwen3.5-122B-A10B`](https://huggingface.co/Qwen/Qwen3.5-122B-A10B)
	checkpoint. It was built with ROCmFPX's compact
	`Q4_0_ROCMFP4_STRIX_LEAN` recipe; the hardware-oriented preset name is left out
	of the public filename.

	\| \| Result \|
	\|---\|---:\|
	\| BF16 mean KLD \| 0.041366 ± 0.002531 \|
	\| Greedy decode \| 28.505 tok/s \|
	\| Sampled decode \| 28.485 tok/s \|
	\| 4,277-token prefill \| 356.900 tok/s \|

	## Files

	\| File \| Purpose \| Size \| SHA-256 \|
	\|---\|---\|---:\|---\|
	\| [`Qwen3.5-122B-A10B-ROCmFP4-iMatrix.gguf`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-ROCmFP4-iMatrix.gguf?download=true) \| Main text model \| 65,184,265,120 bytes \| `9f44eb8a8693f46af6e1b06f6219229eb074c1ec5527798e8a18d68034b381c8` \|
	\| [`Qwen3.5-122B-A10B-ROCmFP4-MTP.gguf`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-ROCmFP4-MTP.gguf?download=true) \| Optional external MTP companion \| 2,294,290,272 bytes \| `f59efaa7c184042a940df322d81921254b8e178d6eefc0fd5cdd7c5b0a9acbe3` \|
	\| [`Qwen3.5-122B-A10B-Bartowski.imatrix`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/Qwen3.5-122B-A10B-Bartowski.imatrix?download=true) \| Calibration matrix \| 358,906,272 bytes \| `e8bfa39dd663e70655035ad53bf715069b7f55175a8877a36bbbe18a0131fed6` \|
	\| [`chat_template.jinja`](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-ROCmFP4-iMatrix-GGUF/resolve/main/chat_template.jinja?download=true) \| Pinned Qwen3.5 chat template \| 7,756 bytes \| `a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715` \|

	The main model is complete and runs independently. The 23-tensor, one-layer
	MTP file is an optional external companion; no MTP layer is embedded in the
	main GGUF. Runtimes call this role a draft model, but `Draft` is omitted from
	the public filename because it does not mean unfinished.

	## Quality: stock and Heretic KLD

	This is the card's only comparison between the stock and Heretic builds. Each
	ROCmFP4 iMatrix model was replayed against saved distributions from its own
	exact BF16 parent, using the same runtime, WikiText-2 token sequence, ROCm0
	backend, two 512-token chunks, and 510 evaluated next-token distributions.

	\| Build \| Mean KLD from its BF16 parent \|
	\|---\|---:\|
	\| Stock iMatrix \| 0.041366 ± 0.002531 \|
	\| [Heretic iMatrix](https://huggingface.co/vmlinux/Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix-GGUF) \| 0.041395 ± 0.002697 \|

	Lower KLD means the quantized model's next-token distribution stayed closer to
	its own BF16 source. It does not rank the underlying stock and Heretic
	models, and it is not an intelligence or benchmark score.

	The two-chunk scope is deliberately bounded: this is the demonstrated-safe
	BF16 workload on the tested 128 GB system. Treat it as a controlled calibration
	comparison, not a completed large-corpus acceptance gate. The Heretic card's
	headline uses a separate 11-chunk replay; its value is therefore not expected
	to match the two-chunk, method-matched figure reported here.

	## Performance

	Measured on a 128 GB AMD Strix Halo system with Vulkan/RADV, 131,072 context,
	parallel 1, batch/ubatch 2048/1024, Q8_0 KV cache, flash attention, and MTP off.
	Values are medians from repeated runs of this stock artifact.

	\| Workload \| Prompt tokens \| Repeats \| Decode \| Prompt processing \|
	\|---\|---:\|---:\|---:\|---:\|
	\| Greedy, 256 generated tokens \| 52 \| 5 \| 28.505 tok/s \| 71.934 tok/s \|
	\| Sampled, 256 generated tokens \| 52 \| 3 \| 28.485 tok/s \| 70.957 tok/s \|
	\| 4,277-token prefill + 128 generated \| 4,277 \| 3 \| 28.107 tok/s \| 356.900 tok/s \|

	These are single-system measurements, not general performance guarantees. The
	MTP companion is included for compatible runtimes, but this card does not claim
	an MTP speed result for the iMatrix artifact.

	## Quantization and matrix provenance

	The model was quantized once from the validated BF16 GGUF using the
	`Q4_0_ROCMFP4_STRIX_LEAN` preset and the importance matrix published with
	[`bartowski/Qwen_Qwen3.5-122B-A10B-GGUF`](https://huggingface.co/bartowski/Qwen_Qwen3.5-122B-A10B-GGUF)
	at revision `f89fb67573c0155d8e5b6556204d86c75cdce0d8`.

	\| Tensor type \| Count \|
	\|---\|---:\|
	\| `Q4_0_ROCMFP4_FAST` \| 457 \|
	\| `Q4_0_ROCMFP4` \| 60 \|
	\| `F32` \| 361 \|
	\| `Q5_K` \| 1 \|

	In this preset, attention K/V tensors retain dual-scale ROCmFP4 protection,
	most transformer weights use the compact FAST layout, and token
	embeddings use Q5_K while the separate output head uses the FAST layout. The
	artifact contains 879 tensors across 48 blocks and no embedded MTP layer.

	The matrix contains 612 entries from 802 × 512-token chunks. Structural
	inspection found finite paired tensors and 37,323 of 37,332 expert count slots
	covered; nine slots had zero observations. A quantizer dry run accepted all 612
	importance entries. The matrix publisher identified the official upstrQwen3-235B-A22Beam
	model but did not pin an immutable upstream weight revision, so that provenance
	limitation is recorded here rather than silently inferred.

	## Run

	Use the ROCmFPX-built `llama-server`, not a stock `llama.cpp` binary:

	```bash
	llama-server \
	--model Qwen3.5-122B-A10B-ROCmFP4-iMatrix.gguf \
	--host 127.0.0.1 --port 8080 \
	-dev Vulkan0 --n-gpu-layers 999 \
	--ctx-size 131072 --parallel 1 \
	--flash-attn on --batch-size 2048 --ubatch-size 1024 \
	--cache-type-k q8_0 --cache-type-v q8_0 \
	--jinja --reasoning-format deepseek
	```

	Adjust context and cache settings for your memory budget. The tested runtime
	was [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX) commit
	`a6a93765f7ce9779c13f9881164a65f7a9f31198`, built in Release mode for
	`gfx1151` with Vulkan and HIP enabled. Correct inference also requires the
	duplicate Qwen3.5 MoE down-scale fix from
	[`llama.cpp` PR #24331](https://github.com/ggml-org/llama.cpp/pull/24331), commit
	[`02810c7`](https://github.com/ggml-org/llama.cpp/commit/02810c7aa89b8100b90b7b0f5e96bc55aafd3d0a): without it, the expert down scale
	is applied twice. The pinned ROCmFPX commit did not yet contain the fix, so the
	tested runtime applied that exact nine-line correction locally; use a newer
	ROCmFPX revision or equivalent runtime that includes it. Results with other
	revisions have not yet been established.

	The GGUF embeds the same chat template shipped separately in this repository,
	so the command uses `--jinja` without an external template path.
	`--reasoning-format deepseek` is the tested llama.cpp parser for this template's
	reasoning output, not a claim that the model is a DeepSeek derivative. The model
	file alone occupies 60.70 GiB; leave additional memory for the runtime and KV
	cache, especially at the tested 131,072-token context.

	To enable the optional companion, append:

	```bash
	--spec-type draft-mtp \
	--spec-draft-model Qwen3.5-122B-A10B-ROCmFP4-MTP.gguf \
	--spec-draft-device Vulkan0 --spec-draft-ngl 999 \
	--spec-draft-type-k f16 --spec-draft-type-v f16 \
	--spec-draft-n-max 2 --spec-draft-p-min 0.6 \
	--spec-draft-p-split 0.10 --spec-draft-backend-sampling
	```

	## Tested system

	\| \| \|
	\|---\|---\|
	\| Platform \| AMD Strix Halo, 128 GB unified memory \|
	\| GPU target \| Radeon 8060S / `gfx1151` \|
	\| Backend \| Vulkan / RADV for serving; ROCm for bounded BF16 KLD collection \|
	\| Kernel \| Linux 6.17.0-1028-oem \|
	\| Mesa \| 25.2.8 \|

	No vision projector is included; treat this release as text-only.

	## Lineage and credits

	- Official model and MTP tensors: [Qwen/Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B), revision `dc4d348443bc740c68e2d77492492c11606384d5`, Apache 2.0.
	- Importance matrix: [bartowski/Qwen_Qwen3.5-122B-A10B-GGUF](https://huggingface.co/bartowski/Qwen_Qwen3.5-122B-A10B-GGUF), revision `f89fb67573c0155d8e5b6556204d86c75cdce0d8`.
	- ROCmFP4 implementation, quantizer, and compatible runtime: [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX).
	- Strix Halo deployment stack: [hec-ovi/llama-vulkan-strix](https://github.com/hec-ovi/llama-vulkan-strix).
	- Conversion, validation, benchmarking, and packaging: `vmlinux`.

	Please preserve this lineage, the Apache 2.0 license, and a description of your
	changes when redistributing derivatives.

	## License

	The distributed model derivative is provided under the upstream Apache License
	2.0, whose text is included as `LICENSE`. Runtime and tooling repositories
	retain their own licenses. No runtime source code is bundled in this model
	repository.