Instructions to use nicolasramos/odooclaw-medium-2.6b-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nicolasramos/odooclaw-medium-2.6b-ft 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 nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nicolasramos/odooclaw-medium-2.6b-ft: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 nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nicolasramos/odooclaw-medium-2.6b-ft: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 nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M

Use Docker

docker model run hf.co/nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M

LM Studio
Jan
Ollama
How to use nicolasramos/odooclaw-medium-2.6b-ft with Ollama:
```
ollama run hf.co/nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
```

Unsloth Studio

How to use nicolasramos/odooclaw-medium-2.6b-ft 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 nicolasramos/odooclaw-medium-2.6b-ft 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 nicolasramos/odooclaw-medium-2.6b-ft to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nicolasramos/odooclaw-medium-2.6b-ft to start chatting

How to use nicolasramos/odooclaw-medium-2.6b-ft with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nicolasramos/odooclaw-medium-2.6b-ft: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": "nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nicolasramos/odooclaw-medium-2.6b-ft with Docker Model Runner:
```
docker model run hf.co/nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M
```

Lemonade

How to use nicolasramos/odooclaw-medium-2.6b-ft with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M

Run and chat with the model

lemonade run user.odooclaw-medium-2.6b-ft-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nicolasramos/odooclaw-medium-2.6b-ft with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nicolasramos/odooclaw-medium-2.6b-ft: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 nicolasramos/odooclaw-medium-2.6b-ft:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nicolasramos/odooclaw-medium-2.6b-ft with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nicolasramos/odooclaw-medium-2.6b-ft: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 "nicolasramos/odooclaw-medium-2.6b-ft: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"

odooclaw-medium-2.6b-ft (v18)

Fine-tune de LFM2.5-2.6B (Liquid AI) para tool-calling en Odoo ERP. Versión v18 — modelo definitivo de la línea Medium.

Rendimiento (baterías de 1000 casos)

Batería (1000)	v18
Conversación (990)	94.2%
Creación (1000)	99.5%
Negocio (1000)	74.0%
Facturas (1000)	71.6%

El v18 es el modelo más equilibrado de la serie: top-2 en las 4 categorías a la vez, sin tradeoffs. Se entrenó con distribución balanceada (coincide con la de evaluación) y variedad natural.

Formato

GGUF Q4_K_M: odooclaw-medium-2.6b-ft-Q4_K_M.gguf
Safetensors (fused) + config

Uso

Servir con llama.cpp usando el chat template sin thinking (el modelo responde directo <tool_call>):

llama-server -m odooclaw-medium-2.6b-ft-Q4_K_M.gguf \
  --chat-template-file chat-template-medium-no-think.jinja \
  --temp 0.0 --jinja

Entrenamiento

Base: LiquidAI/LFM2.5-2.6B
Dataset: 49.301 ejemplos balanceados (TEXTO 32%, search 14%, create_sale_order 9%, find_pending_invoices 9%, create_lead 7%, create_task 6%, cancel_invoice 6%, create 5%, create_vendor_invoice 5%, register_payment 4.4%, find_partner 2.5%)
QLoRA 4-bit, 2 épocas, train_loss 0.0608

Downloads last month: -

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for nicolasramos/odooclaw-medium-2.6b-ft

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(61)

this model