Instructions to use kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Llama new
LM Studio
Jan

vLLM

How to use kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
```
Unsloth Desktop

How to use kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.LFM2-8B-A1B-ROCmFPX-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-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/LFM2-8B-A1B-ROCmFPX-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/LFM2-8B-A1B-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/LFM2-8B-A1B-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/LFM2-8B-A1B-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"

kingjones777 commited on Aug 17

Commit

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README.md +57 -0

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	@@ -0,0 +1,57 @@

+---
+license: other
+license_name: lfm1.0
+base_model: LiquidAI/LFM2-8B-A1B
+base_model_relation: quantized
+tags: [gguf, llama.cpp, rocm, gfx1151, strix-halo, amd, ryzen-ai-max-395, rocmfp4, rocmfpx, lfm2, liquid, moe]
+language: [en]
+pipeline_tag: text-generation
+---
+> ### ⚠️ STOCK `llama.cpp` WILL NOT LOAD THESE
+>
+> The `ROCmFP4` / `ROCmFPX` quant types exist only in
+> [`charlie12345/ROCmFPX`](https://github.com/charlie12345/ROCmFPX) — stock llama.cpp reports
+> `invalid ggml type`. Ignore the auto-generated "Use this model" commands above.
+# LFM2-8B-A1B — ROCmFP4 / ROCmFPX GGUF
+**The first ROCmFP4 quantizations of LFM2-8B-A1B.** Built for **AMD gfx1151
+(Ryzen AI MAX+ 395 / Strix Halo)**. 24 layers · hidden 2048 · **32 experts**, ~1B 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 | 4.41 GiB | 4.54 | **146.61** | 150.64 / 146.32 / 146.61 | smallest — best fit for a 128 GB Strix Halo |
+| `Q6_0_ROCMFPX_AGENT` | 114 | 7.14 GiB | 7.35 | **104.09** | 104.09 / 104.34 / 103.95 | agent/tool-call routing at 6-bit |
+| `Q8_0_ROCMFPX` | 111 | 8.02 GiB | 8.26 | **102.97** | 103.77 / 102.91 / 102.97 | highest fidelity, plain routing |
+| `Q8_0_ROCMFPX_AGENT` | 115 | 8.13 GiB | 8.37 | **104.31** | 104.31 / 103.87 / 104.78 | 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: `unsloth/LFM2-8B-A1B-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.