Instructions to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF with Ollama:
```
ollama run hf.co/luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-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": "luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M
```

Lemonade

How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-2B-catalan-chat-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use luispoveda93/MiniCPM5-2B-catalan-chat-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf luispoveda93/MiniCPM5-2B-catalan-chat-v2-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 "luispoveda93/MiniCPM5-2B-catalan-chat-v2-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"

MiniCPM5-2B-catalan-chat-v2 — GGUF

GGUF conversions of luispoveda93/MiniCPM5-2B-catalan-chat-v2 — round 2: a LoRA fine-tune of luispoveda93/MiniCPM5-2B-catalan-chat on 23.4K multi-turn Catalan conversations from BSC-LT/ALIA-2606-SFT (CC-BY-4.0), focused on chat behaviour.

Converted with llama.cpp convert_hf_to_gguf.py (fp16) and quantized with llama-quantize (release b10067). The chat template is embedded in the GGUF metadata (tokenizer.chat_template).

Available quants

File	Quant	Size	Notes
MiniCPM5-2B-catalan-chat-v2-Q4_K_M.gguf	Q4_K_M	1.6 GB	Best size/quality trade-off
MiniCPM5-2B-catalan-chat-v2-Q8_0.gguf	Q8_0	2.7 GB	Near-lossless
MiniCPM5-2B-catalan-chat-v2-f16.gguf	F16	5.0 GB	Reference / re-quantization source

Usage

llama.cpp

llama-server -m MiniCPM5-2B-catalan-chat-v2-Q4_K_M.gguf --port 8080

Ollama

FROM MiniCPM5-2B-catalan-chat-v2-Q4_K_M.gguf
SYSTEM "Ets un assistent conversacional que respon sempre en català."

ollama create minicpm5-catalan-v2 -f Modelfile
ollama run minicpm5-catalan-v2

Details

Base model (round 2): luispoveda93/MiniCPM5-2B-catalan-chat — round-1 LoRA on openbmb/MiniCPM5-2B (2.5B params, Llama architecture, vocab 130,560)
Round-2 training: 23.4K multi-turn Catalan conversations from BSC-LT/ALIA-2606-SFT (multi-turn augmentation, instruction-following, mentor-ca, dolly-ca, CoQCat, identity, system-prompt multi-turn), LoRA r=32/α=64, lr 1e-4, 1 epoch, ~326 steps; final eval loss 1.383, token accuracy 0.704 (metrics)
License: base model Apache-2.0; round-2 data CC-BY-4.0; round-1 lineage contains InstruCAT (CC-BY-NC-ND-4.0, non-commercial)
Limitations: 2.5B params; chat behaviour focus — task-QA skills come from round 1's single-turn data

Downloads last month: 96

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit

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