Instructions to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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 kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

Use Docker

docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

LM Studio
Jan

vLLM

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

Ollama

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF with Ollama:

ollama run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

Unsloth Studio

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF to start chatting

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
```

Lemonade

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

Run and chat with the model

lemonade run user.Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF-Q8_0_ROCMFPX

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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/Mistral-Small-4-119B-ROCmFPX-Q8_0-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"

⛔ THIS BUILD DOES NOT FIT ON A 128 GB STRIX HALO

llama.cpp reports 113.03 GiB addressable on a Ryzen AI MAX+ 395. These 8-bit builds are 114.38 GiB and 116.15 GiB. Attempting -ngl 999 hard-wedges the machine — we did it twice: a KFD SVM D-state livelock (svm_range_cpu_invalidate_pagetables) that survives a GPU reset and needs a power cycle. -fit off does not save you; it only stops llama.cpp from shrinking the model, so it allocates until the driver dies.

On a single 128 GB Strix Halo, use the 4-bit build (63.07 GiB, 37.86 tok/s). These 8-bit builds are for machines with more memory, or for CPU / partial-offload inference.

Mistral-Small-4-119B-A6.5B — ROCmFPX 8-bit GGUF

An 8-bit ROCmFPX quantization built from BF16 (222 GiB) — a lossless source, not a requantization of a lower-bit build. 119B total / 6.5B active MoE.


File	`Mistral-Small-4-119B-2603-Q8_0_ROCMFPX.gguf`
Size	114.38 GiB
BPW	8.26
ftype	`Q8_0_ROCMFPX` (111)
Tensors	579

Built with --output-tensor-type q8_0 --token-embedding-type q8_0 --tensor-type shexp=q8_0. The shexp override matched 108 shared-expert tensors (confirmed in the dry-run receipt — a --tensor-type pattern that matches nothing is a silent no-op, so we check the count).

⛔ Requires a llama.cpp with the ROCmFPX quant types

Q8_0_ROCMFPX (111) / Q8_0_ROCMFPX_AGENT (115) exist only in charlie12345/ROCmFPX. Stock llama.cpp reports invalid ggml type 103. Ignore the auto-generated "Use this model" commands above.

All quant variants

variant	ftype	size	bpw	GPU on 128 GB Strix Halo	decode
4-bit COHERENT	102	63.07 GiB	4.55	✅ fits	37.86 tok/s
8-bit AGENT	115	116.15 GiB	8.39	⛔ does not fit	not measurable on this box
8-bit plain	111	114.38 GiB	8.26	⛔ does not fit	not measurable on this box

Repos: 4-bit · 8-bit AGENT · 8-bit plain

Verified

Structural: GGUF v3, 579 tensors, 59 KV pairs — matches the source topology.
Correctness (CPU-only, -ngl 0): 17×23 ⇒ ✅ 391 · capital of Japan ⇒ ✅ Tokyo. Loaded in 256 s from disk.

What was NOT measured

⛔ No GPU decode benchmark. The build does not fit in addressable GPU memory on our hardware, so we have no tok/s figure for it and do not quote one.
No perplexity run, no quality A/B against the source, no long-context or tool-calling tests.

Base model licence inherited; credit for the model goes to its authors.

Downloads last month: 74

GGUF

Model size

119B params

Architecture

mistral4

Hardware compatibility

8-bit

Model tree for kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF

Base model

mistralai/Mistral-Small-4-119B-2603

Quantized

(34)

this model

kingjones777
/

Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF

⛔ THIS BUILD DOES NOT FIT ON A 128 GB STRIX HALO

Mistral-Small-4-119B-A6.5B — ROCmFPX 8-bit GGUF

⛔ Requires a llama.cpp with the ROCmFPX quant types

All quant variants

Verified

What was NOT measured

Model tree for kingjones777/Mistral-Small-4-119B-ROCmFPX-Q8_0-GGUF