Instructions to use amkyawdev/amkyaw-dev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkyawdev/amkyaw-dev-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="amkyawdev/amkyaw-dev-v1",
	filename="amkyaw-coder-1.5b-instruct.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 amkyawdev/amkyaw-dev-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amkyawdev/amkyaw-dev-v1
# Run inference directly in the terminal:
llama-cli -hf amkyawdev/amkyaw-dev-v1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf amkyawdev/amkyaw-dev-v1
# Run inference directly in the terminal:
llama-cli -hf amkyawdev/amkyaw-dev-v1

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 amkyawdev/amkyaw-dev-v1
# Run inference directly in the terminal:
./llama-cli -hf amkyawdev/amkyaw-dev-v1

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 amkyawdev/amkyaw-dev-v1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amkyawdev/amkyaw-dev-v1

Use Docker

docker model run hf.co/amkyawdev/amkyaw-dev-v1

LM Studio
Jan
Ollama
How to use amkyawdev/amkyaw-dev-v1 with Ollama:
```
ollama run hf.co/amkyawdev/amkyaw-dev-v1
```

Unsloth Studio new

How to use amkyawdev/amkyaw-dev-v1 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 amkyawdev/amkyaw-dev-v1 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 amkyawdev/amkyaw-dev-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for amkyawdev/amkyaw-dev-v1 to start chatting

Pi new

How to use amkyawdev/amkyaw-dev-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf amkyawdev/amkyaw-dev-v1

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": "amkyaw-dev-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use amkyawdev/amkyaw-dev-v1 with Docker Model Runner:
```
docker model run hf.co/amkyawdev/amkyaw-dev-v1
```

Lemonade

How to use amkyawdev/amkyaw-dev-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amkyawdev/amkyaw-dev-v1

Run and chat with the model

lemonade run user.amkyaw-dev-v1-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

amkyawdev/amkyaw-dev-v1

Model Overview

Model Name: amkyaw-coder-1.5b-instruct
Type: Code Generation / Instruction Following
Size: 1.5B parameters
Format: GGUF (quantized)

Quick Start

# Run the model
ollama run amkyawdev/amkyaw-dev-v1

# Or run with specific tag
ollama run amkyawdev/amkyaw-dev-v1:latest

Features

Code generation
Instruction following
Burmese language support
English language support

System Requirements

Ollama installed
At least 2GB RAM available
No GPU required (runs on CPU)

Configuration

Parameter	Value
Temperature	0.8
Top P	0.9
Top K	40
Context Length	4096

Usage Examples

import ollama

response = ollama.generate(
    model='amkyawdev/amkyaw-dev-v1',
    prompt='Write a Python function to calculate factorial'
)
print(response['response'])

License

See Hugging Face for license information.

Troubleshooting

If you encounter issues:

Make sure Ollama is running: ollama serve
Check model is installed: ollama list
Try restarting Ollama: pkill ollama && ollama serve

Downloads last month: 108

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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