Instructions to use chronorus/chatbot-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chronorus/chatbot-poc with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chronorus/chatbot-poc",
	filename="llama3.2-typhoon2-3b-instruct.Q8_0.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 chronorus/chatbot-poc 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 chronorus/chatbot-poc:Q8_0
# Run inference directly in the terminal:
llama cli -hf chronorus/chatbot-poc:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chronorus/chatbot-poc:Q8_0
# Run inference directly in the terminal:
llama cli -hf chronorus/chatbot-poc:Q8_0

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 chronorus/chatbot-poc:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf chronorus/chatbot-poc:Q8_0

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 chronorus/chatbot-poc:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chronorus/chatbot-poc:Q8_0

Use Docker

docker model run hf.co/chronorus/chatbot-poc:Q8_0

LM Studio
Jan
Ollama
How to use chronorus/chatbot-poc with Ollama:
```
ollama run hf.co/chronorus/chatbot-poc:Q8_0
```
Unsloth Desktop

How to use chronorus/chatbot-poc with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chronorus/chatbot-poc:Q8_0

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": "chronorus/chatbot-poc:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use chronorus/chatbot-poc with Docker Model Runner:
```
docker model run hf.co/chronorus/chatbot-poc:Q8_0
```

Lemonade

How to use chronorus/chatbot-poc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chronorus/chatbot-poc:Q8_0

Run and chat with the model

lemonade run user.chatbot-poc-Q8_0

List all available models

lemonade list

Hermes Agent

How to use chronorus/chatbot-poc with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chronorus/chatbot-poc:Q8_0

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 chronorus/chatbot-poc:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use chronorus/chatbot-poc with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chronorus/chatbot-poc:Q8_0

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 "chronorus/chatbot-poc:Q8_0" \
  --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"

chatbot-poc

File size: 1,297 Bytes

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---
license: apache-2.0
library_name: llama-cpp-python
tags:
  - llama
  - instruction-tuned
  - thai
  - gguf
  - quantized
  - q8
  - rag
  - chatbot
language:
  - th
---

# Llama 3.2 Typhoon2 3B Instruct (GGUF Q8_0)

Fine-tuned Thai instruction-following model quantized to GGUF Q8_0 format for efficient inference.

## Model Details

- **Base Model**: typhoon-ai/llama3.2-typhoon2-3b-instruct
- **Format**: GGUF (Q8_0 quantization)
- **Parameters**: 3 billion
- **Language**: Thai
- **Use Case**: Context-aware Q&A, RAG systems, chatbots

## Training

- **Framework**: Unsloth
- **Method**: Supervised Fine-Tuning (SFT)
- **Training Data**: Thai instruction-following dataset with negative samples for strictness
- **Optimization**: LoRA + 4-bit quantization during training

## Inference

### Using llama-cpp-python

```python
from llama_cpp import Llama

llm = Llama(
    model_path="model.gguf",
    n_ctx=4096,
    n_gpu_layers=0,
)

response = llm(prompt, max_tokens=256, temperature=0.0)
```

### Docker Deployment (EKS)

See deployment guide in the chat-inference Helm chart.

## Performance

- **Quantization**: Q8_0 (8-bit)
- **Model Size**: ~3.3 GB
- **Inference Speed (CPU)**: ~2-5 tokens/sec (t3.xlarge)
- **Recommended CPU**: 2-4 cores, 4-6 GB RAM

## License

Apache License 2.0