Instructions to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-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-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-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-ROCmFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-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-ROCmFP4-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-ROCmFP4-GGUF to start chatting

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Mistral-Small-4-119B-ROCmFP4-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Mistral-Small-4-119B-ROCmFP4-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-ROCmFP4-GGUF:Q4_0_ROCMFP

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-ROCmFP4-GGUF:Q4_0_ROCMFP" \
  --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.cpp WILL NOT LOAD THIS MODEL

🚀 37.86 tok/s on a 119B MoE — 63.07 GiB, 7 GiB smaller than UD-Q4_K_XL.

Mistral-Small-4-119B-A6.5B — ROCmFP4 (tier 102 `COHERENT`) GGUF

A 4-bit ROCmFP4 quantization for AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo), quantized from BF16 (222 GiB) — a lossless source, not a requantization of a lower-bit build.


File	`Mistral-Small-4-119B-2603-Q4_0_ROCMFP4_COHERENT.gguf (sharded)`
Size	63.07 GiB
BPW	4.55
ftype	`Q4_0_ROCMFP4_COHERENT` (102)

⛔ Requires a llama.cpp with the ROCmFP4 quant types

Q4_0_ROCMFP4_COHERENT (ftype 102) exists only in charlie12345/ROCmFPX, not upstream llama.cpp. Ignore the auto-generated "Use this model" commands above.

Files — where each build lives

build	ftype	size	where
4-bit COHERENT (2 shards)	102	63.07 GiB	in this repo
8-bit plain	111	114.38 GiB	in this repo · also at `…-ROCmFPX-Q8_0-GGUF`
8-bit AGENT	115	116.15 GiB	➡️ `…-ROCmFPX-Q8_0-AGENT-GGUF`

The AGENT build is hosted in its own repo rather than mirrored here — at ~116 GiB the duplication is not worth it, and (see the warning above) neither 8-bit build fits in GPU memory on a 128 GB Strix Halo. For a single Strix Halo, use the 4-bit build in this repo.

The separate per-quant repos exist so a user searching HF for a specific quant finds it directly.

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

Measured

Ryzen AI MAX+ 395 (gfx1151, 128 GB unified, ROCm 7.2.4). Median of 3+, warm-up discarded, otherwise-idle box. Correctness at the model's official sampling.

build	size	decode (median)	range
this build	63.07 GiB	37.86	[37.83 – 38.65] (tight)
UD-Q4_K_XL	70 GiB	18.75	[15.99 – 34.30] (wide)

Ranges disjoint (34.30 < 37.83). The median ratio looks like +102%, but the baseline's own variance is large — we report the win without leaning on that headline number. Note the baseline is UD-Q4_K_XL, not Q4_K_M.

Correctness: 17×23 ⇒ ✅ 391 · capital of Japan ⇒ ✅ Tokyo · days in 2024 ⇒ ✅ 366

Per-tensor types (audited in the finished file)

output.weight Q6_K · token_embd Q6_K · shexp 108× Q8_0 (shared-expert protection) · router 36× F32 · norms 145× F32 · bulk TYPE_100 (288)

119B total / 6.5B active. Built with --tensor-type shexp=q8_0; shared experts are dense (they see every token) so their error is systematic, not averaged.

What was NOT measured

No perplexity run, and no quality A/B against the baseline or the source. The checks above are memorized-fact prompts — necessary but not sufficient; a damaged model can pass them.
No long-context testing.
No tool-calling evaluation.

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

Downloads last month: 50

GGUF

Model size

119B params

Architecture

mistral4

Hardware compatibility

4-bit

8-bit

Model tree for kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF

Base model

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

Quantized

(34)

this model

kingjones777
/

Mistral-Small-4-119B-ROCmFP4-GGUF

⚠️ STOCK `llama.cpp` WILL NOT LOAD THIS MODEL

Mistral-Small-4-119B-A6.5B — ROCmFP4 (tier 102 `COHERENT`) GGUF

⛔ Requires a llama.cpp with the ROCmFP4 quant types

Files — where each build lives

All quant variants

Measured

Per-tensor types (audited in the finished file)

What was NOT measured

Model tree for kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF

⚠️ STOCK llama.cpp WILL NOT LOAD THIS MODEL

Mistral-Small-4-119B-A6.5B — ROCmFP4 (tier 102 COHERENT) GGUF

⛔ Requires a llama.cpp with the ROCmFP4 quant types

Files — where each build lives

All quant variants

Measured

Per-tensor types (audited in the finished file)

What was NOT measured

Model tree for kingjones777/Mistral-Small-4-119B-ROCmFP4-GGUF

⚠️ STOCK `llama.cpp` WILL NOT LOAD THIS MODEL

Mistral-Small-4-119B-A6.5B — ROCmFP4 (tier 102 `COHERENT`) GGUF