Text Generation
GGUF
Spanish
English
mate
code
tool-calling
reasoning
argentina
Mixture of Experts
quantized
q4_k_s
conversational
Instructions to use fsantibanezleal/mate-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use fsantibanezleal/mate-v2 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 fsantibanezleal/mate-v2:Q4_K_S # Run inference directly in the terminal: llama cli -hf fsantibanezleal/mate-v2:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fsantibanezleal/mate-v2:Q4_K_S # Run inference directly in the terminal: llama cli -hf fsantibanezleal/mate-v2:Q4_K_S
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 fsantibanezleal/mate-v2:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf fsantibanezleal/mate-v2:Q4_K_S
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 fsantibanezleal/mate-v2:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf fsantibanezleal/mate-v2:Q4_K_S
Use Docker
docker model run hf.co/fsantibanezleal/mate-v2:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use fsantibanezleal/mate-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fsantibanezleal/mate-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fsantibanezleal/mate-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fsantibanezleal/mate-v2:Q4_K_S
- Ollama
How to use fsantibanezleal/mate-v2 with Ollama:
ollama run hf.co/fsantibanezleal/mate-v2:Q4_K_S
- Unsloth Studio
How to use fsantibanezleal/mate-v2 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 fsantibanezleal/mate-v2 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 fsantibanezleal/mate-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fsantibanezleal/mate-v2 to start chatting
- Pi
How to use fsantibanezleal/mate-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fsantibanezleal/mate-v2:Q4_K_S
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": "fsantibanezleal/mate-v2:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use fsantibanezleal/mate-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fsantibanezleal/mate-v2:Q4_K_S
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 "fsantibanezleal/mate-v2:Q4_K_S" \ --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 fsantibanezleal/mate-v2 with Docker Model Runner:
docker model run hf.co/fsantibanezleal/mate-v2:Q4_K_S
- Lemonade
How to use fsantibanezleal/mate-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fsantibanezleal/mate-v2:Q4_K_S
Run and chat with the model
lemonade run user.mate-v2-Q4_K_S
List all available models
lemonade list
- Hermes Agent
How to use fsantibanezleal/mate-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf fsantibanezleal/mate-v2:Q4_K_S
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 fsantibanezleal/mate-v2:Q4_K_S
Run Hermes
hermes
- Atomic Chat
File size: 1,729 Bytes
a20816d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # Mate v2
# Created by Gonzalo Rocca — https://gonzalorocca.com.ar
# Base: Qwen3.5-35B-A3B (Apache 2.0)
# Licensed under Apache 2.0 — see ../NOTICE for third-party attributions.
FROM ./mate-v2-Q4_K_S.gguf
# Vision encoder (mmproj) — necesario para procesar imagenes
# Habilitar cuando el archivo este disponible:
# ADAPTER ./mmproj-mate-v2.gguf
# Qwen3.5 chat template (ChatML)
TEMPLATE """{{- range $i, $_ := .Messages }}
{{- if eq .Role "system" }}<|im_start|>system
{{ .Content }}<|im_end|>
{{ else if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ .Content }}<|im_end|>
{{ end }}
{{- end }}{{- if .Messages }}<|im_start|>assistant
{{ end }}"""
# Stops Qwen ChatML
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
# Sampling — modo coding precision (recomendado por Qwen oficial)
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER repeat_penalty 1.05
PARAMETER presence_penalty 0.0
# Context default 128K (sweet spot velocidad/contexto en RTX 3090 24GB)
# Subir a 200000 si necesitas mas; bajar a 32768 para inference mas rapida.
PARAMETER num_ctx 131072
PARAMETER num_gpu 999
SYSTEM """You are Mate, an open-source AI coding assistant created by Gonzalo Rocca (gonzalorocca.com.ar). You write clean, efficient code, explain your reasoning step by step, and call functions/tools when needed. You can analyze code screenshots, error messages, UI mockups, and stack traces directly when images are provided. You support OpenAI-style tool calling and MCP (Model Context Protocol). You speak both English and Argentinian Spanish (vos / voseo). When the user writes in Argentinian Spanish, respond in kind."""
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