Instructions to use vinod-halaharvi/ascii-to-go-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vinod-halaharvi/ascii-to-go-coder with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vinod-halaharvi/ascii-to-go-coder",
	filename="ascii-to-go-coder-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vinod-halaharvi/ascii-to-go-coder 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 vinod-halaharvi/ascii-to-go-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinod-halaharvi/ascii-to-go-coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vinod-halaharvi/ascii-to-go-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinod-halaharvi/ascii-to-go-coder: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 vinod-halaharvi/ascii-to-go-coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vinod-halaharvi/ascii-to-go-coder: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 vinod-halaharvi/ascii-to-go-coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vinod-halaharvi/ascii-to-go-coder:Q4_K_M

Use Docker

docker model run hf.co/vinod-halaharvi/ascii-to-go-coder:Q4_K_M

LM Studio
Jan

vLLM

How to use vinod-halaharvi/ascii-to-go-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vinod-halaharvi/ascii-to-go-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vinod-halaharvi/ascii-to-go-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vinod-halaharvi/ascii-to-go-coder:Q4_K_M

Ollama
How to use vinod-halaharvi/ascii-to-go-coder with Ollama:
```
ollama run hf.co/vinod-halaharvi/ascii-to-go-coder:Q4_K_M
```

Unsloth Studio

How to use vinod-halaharvi/ascii-to-go-coder 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 vinod-halaharvi/ascii-to-go-coder 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 vinod-halaharvi/ascii-to-go-coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vinod-halaharvi/ascii-to-go-coder to start chatting

How to use vinod-halaharvi/ascii-to-go-coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinod-halaharvi/ascii-to-go-coder:Q4_K_M

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": "vinod-halaharvi/ascii-to-go-coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vinod-halaharvi/ascii-to-go-coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinod-halaharvi/ascii-to-go-coder: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 vinod-halaharvi/ascii-to-go-coder:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use vinod-halaharvi/ascii-to-go-coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinod-halaharvi/ascii-to-go-coder: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 "vinod-halaharvi/ascii-to-go-coder: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"

Docker Model Runner
How to use vinod-halaharvi/ascii-to-go-coder with Docker Model Runner:
```
docker model run hf.co/vinod-halaharvi/ascii-to-go-coder:Q4_K_M
```

Lemonade

How to use vinod-halaharvi/ascii-to-go-coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinod-halaharvi/ascii-to-go-coder:Q4_K_M

Run and chat with the model

lemonade run user.ascii-to-go-coder-Q4_K_M

List all available models

lemonade list

ASCII-to-Go Coder

A fine-tuned model that generates working Go code from ASCII architecture diagrams.

What it does

Give it an ASCII box diagram describing a system architecture, and it generates complete, compilable Go code implementing that system.

Input:

[Producer] --> [Channel] --> [Worker 1]
                         --> [Worker 2]
                         --> [Worker 3]
                              |
                         [Results Channel]
                              |
                         [Collector]

Output: A complete Go program with goroutines, channels, WaitGroups, and proper shutdown handling.

Training

Base model: Qwen2.5-Coder-7B-Instruct
Method: LoRA (r=16, 0.53% of parameters trainable)
Dataset: 76 hand-written ASCII diagram → Go code pairs
Training time: ~13 minutes on NVIDIA L4 (24GB)
Final loss: 0.011
Token accuracy: 99.8%

Patterns covered

The training data covers real-world Go patterns including: HTTP servers, middleware chains, worker pools, pub/sub, WebSocket chat, reverse proxies, JWT authentication, circuit breakers, LRU caches, ring buffers, tries, priority queues, state machines, event sourcing, SSE streaming, gzip compression, generics, channel pipelines, graceful shutdown, rate limiting, fan-in/fan-out, and more.

How to run

With llama.cpp:

llama-server \
    -hf vinod-halaharvi/ascii-to-go-coder \
    -ngl 999 --host 0.0.0.0 --port 8080 --ctx-size 4096

Then open http://localhost:8080 and paste an ASCII diagram.

Limitations

Trained on only 76 examples — may struggle with patterns not in the training set
Generated code compiles but may contain logical bugs in complex scenarios
Works best with standard library patterns; less reliable with third-party libraries
This is a learning project, not production-grade

Hardware requirements

Minimum: 6GB VRAM (Q4_K_M quantization)
Recommended: NVIDIA GPU with 8GB+ VRAM
Also runs on: CPU (slower), Apple Silicon (via Metal)

Downloads last month: 6

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for vinod-halaharvi/ascii-to-go-coder

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(718)

this model