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."
)

llama-cpp-python

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

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio new

How to use chronorus/chatbot-poc 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 chronorus/chatbot-poc 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 chronorus/chatbot-poc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chronorus/chatbot-poc to start chatting

Pi new

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-server -hf chronorus/chatbot-poc:Q8_0

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": "chatbot-poc"
        }
      ]
    }
  }
}

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

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

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

Downloads last month: 30

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

8-bit

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