Instructions to use johnbean393/chiboard-1-m0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use johnbean393/chiboard-1-m0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="johnbean393/chiboard-1-m0-GGUF",
	filename="Chiboard-M0-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use johnbean393/chiboard-1-m0-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 johnbean393/chiboard-1-m0-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf johnbean393/chiboard-1-m0-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

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 johnbean393/chiboard-1-m0-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

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 johnbean393/chiboard-1-m0-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

Use Docker

docker model run hf.co/johnbean393/chiboard-1-m0-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use johnbean393/chiboard-1-m0-GGUF with Ollama:
```
ollama run hf.co/johnbean393/chiboard-1-m0-GGUF:Q6_K
```

Unsloth Studio

How to use johnbean393/chiboard-1-m0-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 johnbean393/chiboard-1-m0-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 johnbean393/chiboard-1-m0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for johnbean393/chiboard-1-m0-GGUF to start chatting

How to use johnbean393/chiboard-1-m0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

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": "johnbean393/chiboard-1-m0-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use johnbean393/chiboard-1-m0-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 johnbean393/chiboard-1-m0-GGUF:Q6_K

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 johnbean393/chiboard-1-m0-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use johnbean393/chiboard-1-m0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf johnbean393/chiboard-1-m0-GGUF:Q6_K

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 "johnbean393/chiboard-1-m0-GGUF:Q6_K" \
  --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 johnbean393/chiboard-1-m0-GGUF with Docker Model Runner:
```
docker model run hf.co/johnbean393/chiboard-1-m0-GGUF:Q6_K
```

Lemonade

How to use johnbean393/chiboard-1-m0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull johnbean393/chiboard-1-m0-GGUF:Q6_K

Run and chat with the model

lemonade run user.chiboard-1-m0-GGUF-Q6_K

List all available models

lemonade list

chiboard-1-m0-GGUF

GGUF exports of johnbean393/chiboard-1-m0, a bootstrap 350M SFT model for Chiboard mistake mining.

Prompt format:

<|startoftext|>{committed_context}<|reserved_6|>{raw_pinyin}<|reserved_7|>{display}<|reserved_8|>{target}<|im_end|>

The source model is trained with completion-only loss on {target}<|im_end|>.

Serve with exactly one <|startoftext|> token. Most runtimes add it automatically, so do not also embed it in the prompt string.

This is M0, a data-generation tool for typing-prefix replay; it is not the shipped final model.

Files

Chiboard-M0-Q6_K.gguf: Q6_K GGUF quantization of johnbean393/chiboard-1-m0.
Chiboard-M0-Q8_0.gguf: Q8_0 GGUF quantization of johnbean393/chiboard-1-m0.

Training

Base model: LiquidAI/LFM2.5-350M-Base
Dataset: johnbean393/chiboard-1-sft
Source model: johnbean393/chiboard-1-m0
Hub target: johnbean393/chiboard-1-m0-GGUF
Training layout: mixed_packed
Max packed length: 4096
Effective batch: 10 x 6 = 60 packed rows per optimizer step
Steps: 14746 / 14746
Final eval loss: 0.17373667657375336
Final eval mean token accuracy: 0.9519697650369391
Train runtime seconds: 3.506e+04
Packed LFM2 short-conv boundary isolation: seq_idx collator enabled

Downloads last month: 61

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

6-bit

8-bit

Inference Providers NEW

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

Model tree for johnbean393/chiboard-1-m0-GGUF

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

johnbean393/chiboard-1-m0

Quantized

(1)

this model