Instructions to use kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF to start chatting

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.LFM2-24B-A2B-ROCmFPX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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/LFM2-24B-A2B-ROCmFPX-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 THESE

The ROCmFP4 / ROCmFPX quant types exist only in charlie12345/ROCmFPX — stock llama.cpp reports invalid ggml type. Ignore the auto-generated "Use this model" commands above.

LFM2-24B-A2B — ROCmFP4 / ROCmFPX GGUF

The first ROCmFP4 quantizations of LFM2-24B-A2B. Built for AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo). 40 layers · hidden 2048 · 64 experts, ~2B active.

Quantized from BF16 GGUF — a lossless source, not a requantization of a lower-bit build.

All four quants — pick by size, not by hoping

file	ftype	size	bpw	decode (median of 3)	runs	best for
`Q4_0_ROCMFP4_COHERENT`	102	12.54 GiB	4.52	95.24	95.37 / 94.90 / 95.24	smallest — best fit for a 128 GB Strix Halo
`Q6_0_ROCMFPX_AGENT`	114	20.47 GiB	7.37	67.51	67.80 / 67.51 / 67.20	agent/tool-call routing at 6-bit
`Q8_0_ROCMFPX`	111	22.92 GiB	8.26	67.37	67.62 / 67.33 / 67.37	highest fidelity, plain routing
`Q8_0_ROCMFPX_AGENT`	115	23.26 GiB	8.38	66.53	66.53 / 66.53 / 66.50	highest fidelity + agent routing

Measured on a Ryzen AI MAX+ 395 (gfx1151, ROCm), -ngl 999 -c 4096 -fa on -fit off, median of 3 with warm-up discarded on an otherwise-idle box.

Correctness: 3/3 on every quant — 17x23 => 391 - capital of Japan => Tokyo - days in 2024 => 366 (max_tokens 1024)

Speculative decoding: None. No MTP / EAGLE / nextn tensors, so there is no speculative speedup to enable and no acceptance length to quote. Source: LiquidAI/LFM2-24B-A2B-GGUF BF16.

ℹ️ Why no "4-bit AGENT"? The AGENT recipes exist only at 3-bit (113), 6-bit (114/117) and 8-bit (115) — the entire 4-bit family (100–106) has no AGENT variant. Rather than hand-roll one and publish it under a name implying it is standard, the 6-bit Q6_0_ROCMFPX_AGENT fills that slot. AGENT routing keeps more tensors at higher precision for tool-call coherence.

ℹ️ Head protection. These GGUFs contain no separate output.weight — the embedding is tied — so --token-embedding-type is what protects the head here and --output-tensor-type is a no-op. Audited in the finished files: token_embd is Q6_K on the 4-bit build and Q8_0 on the 6/8-bit builds.

What was NOT measured

No perplexity run, and no quality A/B against the BF16 source.
No long-context testing. · No tool-calling evaluation — note the AGENT builds are named for tool-call routing but we did not benchmark tool use.

Base model licence inherited; all credit for the weights goes to Liquid AI.

Downloads last month: 213

GGUF

Model size

24B params

Architecture

lfm2moe

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF

Base model

LiquidAI/LFM2-24B-A2B

Quantized

(24)

this model

kingjones777
/

LFM2-24B-A2B-ROCmFPX-GGUF

⚠️ STOCK `llama.cpp` WILL NOT LOAD THESE

LFM2-24B-A2B — ROCmFP4 / ROCmFPX GGUF

All four quants — pick by size, not by hoping

What was NOT measured

Model tree for kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF

⚠️ STOCK llama.cpp WILL NOT LOAD THESE

LFM2-24B-A2B — ROCmFP4 / ROCmFPX GGUF

All four quants — pick by size, not by hoping

What was NOT measured

Model tree for kingjones777/LFM2-24B-A2B-ROCmFPX-GGUF

⚠️ STOCK `llama.cpp` WILL NOT LOAD THESE