Instructions to use LiquidAI/LFM2.5-ColBERT-350M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-ColBERT-350M-GGUF with sentence-transformers:

from pylate import models

queries = [
    "Which planet is known as the Red Planet?",
    "What is the largest planet in our solar system?",
]

documents = [
    ["Mars is the Red Planet.", "Venus is Earth's twin."],
    ["Jupiter is the largest planet.", "Saturn has rings."],
]

model = models.ColBERT(model_name_or_path="LiquidAI/LFM2.5-ColBERT-350M-GGUF")

queries_emb = model.encode(queries, is_query=True)
docs_emb = model.encode(documents, is_query=False)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M

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

How to use LiquidAI/LFM2.5-ColBERT-350M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LiquidAI/LFM2.5-ColBERT-350M-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": "LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use LiquidAI/LFM2.5-ColBERT-350M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-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 LiquidAI/LFM2.5-ColBERT-350M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LiquidAI/LFM2.5-ColBERT-350M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LiquidAI/LFM2.5-ColBERT-350M-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 "LiquidAI/LFM2.5-ColBERT-350M-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"

EdoardoMosca commited on Jun 18

Commit

ea4ee4e

verified ·

1 Parent(s): 1f7f090

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -52,7 +52,7 @@ LFM2.5-ColBERT-350M is a late interaction retriever with best-in-class multiling
 - Inference speed is **on par with much smaller models**, thanks to the efficient LFM2 backbone.
 - You can use it as a **drop-in replacement** in your current RAG pipelines to improve performance.
-Find more information about LFM2.5-ColBERT-350M in our [blog post](https://liquid-ai-v3-c7c6d49467ac-bf50aea57dc57.webflow.io/blog/lfm2-5-re).
 ## 🏃 How to run

 - Inference speed is **on par with much smaller models**, thanks to the efficient LFM2 backbone.
 - You can use it as a **drop-in replacement** in your current RAG pipelines to improve performance.
+Find more information about LFM2.5-ColBERT-350M in our [blog post](https://liquid-ai-v3-c7c6d49467ac-bf50aea57dc57.webflow.io/blog/lfm2-5-retrievers).
 ## 🏃 How to run