Instructions to use NANI-Nithin/LFM2.5-350M-RLCD-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 NANI-Nithin/LFM2.5-350M-RLCD-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 NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

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 NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

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 NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use NANI-Nithin/LFM2.5-350M-RLCD-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/LFM2.5-350M-RLCD-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

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": "NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/LFM2.5-350M-RLCD-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/LFM2.5-350M-RLCD-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-350M-RLCD-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/LFM2.5-350M-RLCD-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 NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

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 NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/LFM2.5-350M-RLCD-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

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 "NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M" \
  --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"

LFM2.5-350M-RLCD · GGUF

A complete llama.cpp quantization sweep of notnotsamuel/LFM2.5-350M-RLCD: 29 quants plus the BF16 conversion, all cut from the same BF16 file, with the importance matrix computed on BF16. Quantized 2026-09-22 with llama.cpp (f3f1a8f).

What this model is

The source repo bundles two things:

The weights of LiquidAI/LFM2.5-350M, byte-for-byte unchanged from upstream revision 9e6c6ccf (verified checksums in the source repo's BASE_MODEL_MANIFEST.json). No fine-tuning, no adapters.
An MIT-licensed Python engine for parallel structured inference ("RLCD"): prefill the context once, reuse attention and convolution state across candidate branches, score all allowed schema values in a batch, and assemble the JSON in Python. For a 28-field extraction benchmark the source reports 8.5–63× lower end-to-end latency than autoregressive generation — with the explicit caveat that JSON validity there comes from programmatic assembly, not from better model decisions.

These GGUF files are the language model only. The RLCD engine is PyTorch code and does not run on GGUF; if you want the parallel constrained-inference method itself, use the source repo. What you get here is a standard, llama.cpp-runnable quantization of exactly the weights that engine loads.

Source spec: Lfm2ForCausalLM · 354M parameters · 16 layers · hidden size 1024 · vocab 65,536 · text-only.

Which file to grab

Q4_K_M (0.21 GB) — the usual default; best quality-per-byte.
Q8_0 (0.35 GB) or BF16 (0.66 GB) — at 350M parameters, near-lossless costs almost nothing. If you are not squeezing a tiny device, just take Q8_0.
IQ2_M (0.13 GB) — the smallest size most people still find usable.
Q1_0 / IQ1_S / IQ1_M — included for completeness; expect substantial degradation.

Usage

# run directly from the Hub
llama-cli -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M -p "Hello"

# serve an OpenAI-compatible endpoint
llama-server -hf NANI-Nithin/LFM2.5-350M-RLCD-GGUF:Q4_K_M

# or download a single file first
huggingface-cli download NANI-Nithin/LFM2.5-350M-RLCD-GGUF LFM2.5-350M-RLCD-Q4_K_M.gguf --local-dir .
llama-cli -m LFM2.5-350M-RLCD-Q4_K_M.gguf -p "Hello"

These are llama.cpp GGUF files. They are not loaded by vLLM, SGLang or transformers the way the source repo is — use the source repo for those.

Available quants

File	Quant	Size	Notes
LFM2.5-350M-RLCD-BF16.gguf	BF16	0.66 GB	Full-precision source; every other file is cut from this one.
LFM2.5-350M-RLCD-Q8_0.gguf	Q8_0	0.35 GB	Effectively lossless. Use when disk and RAM are not the constraint.
LFM2.5-350M-RLCD-Q6_K.gguf	Q6_K	0.27 GB	Near-lossless; the last stop before quality becomes measurable.
LFM2.5-350M-RLCD-Q5_K_M.gguf	Q5_K_M	0.24 GB	Very good quality, noticeably smaller than Q6_K.
LFM2.5-350M-RLCD-Q5_K_S.gguf	Q5_K_S	0.24 GB	Slightly smaller than Q5_K_M for a slight quality cost.
LFM2.5-350M-RLCD-Q5_1.gguf	Q5_1	0.25 GB	Legacy. Prefer Q5_K_M.
LFM2.5-350M-RLCD-Q5_0.gguf	Q5_0	0.24 GB	Legacy. Prefer Q5_K_M.
LFM2.5-350M-RLCD-Q4_K_M.gguf	Q4_K_M	0.21 GB	The usual default. Best quality-per-byte for most people.
LFM2.5-350M-RLCD-Q4_K_S.gguf	Q4_K_S	0.21 GB	A little smaller than Q4_K_M, a little worse.
LFM2.5-350M-RLCD-IQ4_NL.gguf	IQ4_NL	0.20 GB	Non-linear 4-bit; good on hardware without fast K-quant kernels.
LFM2.5-350M-RLCD-IQ4_XS.gguf	IQ4_XS	0.20 GB	Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size.
LFM2.5-350M-RLCD-Q4_1.gguf	Q4_1	0.22 GB	Legacy. Prefer Q4_K_M.
LFM2.5-350M-RLCD-Q4_0.gguf	Q4_0	0.20 GB	Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this.
LFM2.5-350M-RLCD-Q3_K_L.gguf	Q3_K_L	0.19 GB	Small, with real quality loss. Usable when RAM is tight.
LFM2.5-350M-RLCD-Q3_K_M.gguf	Q3_K_M	0.18 GB	Smaller again; noticeable degradation.
LFM2.5-350M-RLCD-IQ3_M.gguf	IQ3_M	0.17 GB	Strong at ~3.7bpw, clearly better than Q3_K_M.
LFM2.5-350M-RLCD-IQ3_S.gguf	IQ3_S	0.17 GB	Slightly smaller than IQ3_M.
LFM2.5-350M-RLCD-Q3_K_S.gguf	Q3_K_S	0.17 GB	Aggressive. Prefer IQ3_M at a similar size.
LFM2.5-350M-RLCD-IQ3_XS.gguf	IQ3_XS	0.16 GB	Aggressive but coherent.
LFM2.5-350M-RLCD-IQ3_XXS.gguf	IQ3_XXS	0.15 GB	Very aggressive; the last coherent step down.
LFM2.5-350M-RLCD-Q2_K.gguf	Q2_K	0.15 GB	Very small, heavily degraded. For experimentation.
LFM2.5-350M-RLCD-Q2_K_S.gguf	Q2_K_S	0.14 GB	Smaller than Q2_K, at a further quality cost.
LFM2.5-350M-RLCD-IQ2_M.gguf	IQ2_M	0.13 GB	The smallest size most people find usable.
LFM2.5-350M-RLCD-IQ2_S.gguf	IQ2_S	0.13 GB	Below the usual usability line.
LFM2.5-350M-RLCD-IQ2_XS.gguf	IQ2_XS	0.12 GB	Experimental.
LFM2.5-350M-RLCD-IQ2_XXS.gguf	IQ2_XXS	0.12 GB	Experimental.
LFM2.5-350M-RLCD-Q2_0.gguf	Q2_0	0.13 GB	Extreme, group-64. Included for completeness.
LFM2.5-350M-RLCD-IQ1_M.gguf	IQ1_M	0.11 GB	Extreme. Expect substantial degradation.
LFM2.5-350M-RLCD-IQ1_S.gguf	IQ1_S	0.10 GB	Extreme. Expect substantial degradation.
LFM2.5-350M-RLCD-Q1_0.gguf	Q1_0	0.09 GB	Extreme. Included for completeness.

Acknowledgements & license

The model weights are LiquidAI's, distributed unchanged through notnotsamuel/LFM2.5-350M-RLCD under the LFM Open License v1.0, which these quantizations inherit. The RLCD engine code in the source repo is MIT-licensed; no code is included in this repo. Benchmark and methodology details for the engine live in the source repo, not here.

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lfm2

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