Instructions to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/LiquidAI-LFM2.5-230M-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/LiquidAI-LFM2.5-230M-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/LiquidAI-LFM2.5-230M-GGUF",
	filename="LFM2.5-230M.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LiquidAI-LFM2.5-230M-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": "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/LiquidAI-LFM2.5-230M-GGUF to start chatting

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

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": "prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-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 prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LiquidAI-LFM2.5-230M-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 "prithivMLmods/LiquidAI-LFM2.5-230M-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"

Docker Model Runner
How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/LiquidAI-LFM2.5-230M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LiquidAI-LFM2.5-230M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LiquidAI-LFM2.5-230M-GGUF-Q4_K_M

List all available models

lemonade list

prithivMLmods commited on 13 days ago

Commit

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@@ -4,11 +4,28 @@ license_name: lfm1.0
 license_link: LICENSE
 base_model:
 - LiquidAI/LFM2.5-230M
 ---
 # **LiquidAI-LFM2.5-230M-GGUF**
-**[LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M)** is [Liquid AI's](https://huggingface.co/LiquidAI) most compact hybrid model to date, a 230-million-parameter, general-purpose instruction-tuned text model built on the LFM2 architecture with extended pre-training (19T tokens) and reinforcement learning, designed specifically for on-device deployment in the tightest memory and compute budgets. Its 14-layer architecture combines 8 double-gated LIV convolution blocks with 6 GQA blocks, supports a 32,768-token context window across 10 languages, and was distilled from the larger LFM2.5-350M before being refined with multi-stage reinforcement learning, making it well-suited for agentic tasks like tool use and data extraction rather than reasoning-heavy workloads such as advanced math, code generation, or creative writing. It delivers strong edge inference throughput — 213 tok/s decode speed on a Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5 — and despite its tiny size, outperforms similarly-scaled competitors like Granite 4.0-350M and LFM2-350M on benchmarks including IFEval (71.71), BFCLv3 (43.26), and Multi-IF (37.70), while supporting native function calling via Pythonic tool calls and ChatML-style chat templates, with deployment options spanning Transformers, vLLM, llama.cpp (GGUF), ONNX, and MLX formats.
 ## Model Files

 license_link: LICENSE
 base_model:
 - LiquidAI/LFM2.5-230M
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+- pt
+- it
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2.5
+- edge
+library_name: transformers
 ---
 # **LiquidAI-LFM2.5-230M-GGUF**
+> **[LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M)** is [Liquid AI's](https://huggingface.co/LiquidAI) most compact hybrid model to date, a 230-million-parameter, general-purpose instruction-tuned text model built on the LFM2 architecture with extended pre-training (19T tokens) and reinforcement learning, designed specifically for on-device deployment in the tightest memory and compute budgets. Its 14-layer architecture combines 8 double-gated LIV convolution blocks with 6 GQA blocks, supports a 32,768-token context window across 10 languages, and was distilled from the larger LFM2.5-350M before being refined with multi-stage reinforcement learning, making it well-suited for agentic tasks like tool use and data extraction rather than reasoning-heavy workloads such as advanced math, code generation, or creative writing. It delivers strong edge inference throughput — 213 tok/s decode speed on a Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5 — and despite its tiny size, outperforms similarly-scaled competitors like Granite 4.0-350M and LFM2-350M on benchmarks including IFEval (71.71), BFCLv3 (43.26), and Multi-IF (37.70), while supporting native function calling via Pythonic tool calls and ChatML-style chat templates, with deployment options spanning Transformers, vLLM, llama.cpp (GGUF), ONNX, and MLX formats.
 ## Model Files