Instructions to use teolm30/Ult1-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teolm30/Ult1-coding with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="teolm30/Ult1-coding")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("teolm30/Ult1-coding", device_map="auto")

llama-cpp-python

How to use teolm30/Ult1-coding with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teolm30/Ult1-coding",
	filename="Ult1-Coding-Q8_0.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 teolm30/Ult1-coding 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 teolm30/Ult1-coding:Q8_0
# Run inference directly in the terminal:
llama cli -hf teolm30/Ult1-coding:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf teolm30/Ult1-coding:Q8_0
# Run inference directly in the terminal:
llama cli -hf teolm30/Ult1-coding: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 teolm30/Ult1-coding:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf teolm30/Ult1-coding: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 teolm30/Ult1-coding:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teolm30/Ult1-coding:Q8_0

Use Docker

docker model run hf.co/teolm30/Ult1-coding:Q8_0

LM Studio
Jan

vLLM

How to use teolm30/Ult1-coding with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teolm30/Ult1-coding"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Ult1-coding",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teolm30/Ult1-coding:Q8_0

SGLang

How to use teolm30/Ult1-coding with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "teolm30/Ult1-coding" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Ult1-coding",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "teolm30/Ult1-coding" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Ult1-coding",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use teolm30/Ult1-coding with Ollama:
```
ollama run hf.co/teolm30/Ult1-coding:Q8_0
```

Unsloth Studio

How to use teolm30/Ult1-coding 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 teolm30/Ult1-coding 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 teolm30/Ult1-coding to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teolm30/Ult1-coding to start chatting

How to use teolm30/Ult1-coding with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teolm30/Ult1-coding: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": "teolm30/Ult1-coding:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teolm30/Ult1-coding with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teolm30/Ult1-coding: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 teolm30/Ult1-coding:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use teolm30/Ult1-coding with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teolm30/Ult1-coding: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 "teolm30/Ult1-coding: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"

Docker Model Runner
How to use teolm30/Ult1-coding with Docker Model Runner:
```
docker model run hf.co/teolm30/Ult1-coding:Q8_0
```

Lemonade

How to use teolm30/Ult1-coding with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teolm30/Ult1-coding:Q8_0

Run and chat with the model

lemonade run user.Ult1-coding-Q8_0

List all available models

lemonade list

Ult1-Coding

A 3-billion-parameter coding specialist -- master-level software engineer.

Based on Qwen2.5-3B-Instruct with an embedded master programmer system prompt containing few-shot coding demonstrations and a coding-focused LoRA adapter (rank 16, 8 target module types).

Usage:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teolm30/Ult1-coding")
tokenizer = AutoTokenizer.from_pretrained("teolm30/Ult1-coding")

messages = [{"role": "user", "content": "Write a Python async web scraper with retry logic"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

The system prompt with few-shot examples is auto-injected by the chat template - no manual system prompt needed.

GGUF: Download Ult1-Coding-Q8_0.gguf for CPU inference with llama.cpp.

Training Data: training_data.json contains 10 coding Q&A pairs (Python, JavaScript, Rust, SQL, TypeScript, Go). Use with train.py on a GPU.

Details:

Base: Qwen2.5-3B-Instruct (3B params)
LoRA: Rank 16, targets q/k/v/o + gate/up/down projections
Context: 32,768 tokens
Focus: Code generation, algorithms, system design, debugging

Downloads last month: 94

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

8-bit

Model tree for teolm30/Ult1-coding

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Adapter

(1306)

this model