Text Generation
Transformers
Safetensors
GGUF
English
qwen
qwen2.5
3b
lora
coding
code
software-engineering
conversational
Instructions to use teolm30/Ult1-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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
| [ | |
| { | |
| "instruction": "Write a Python function to check if a string is a palindrome ignoring case and non-alphanumeric characters.", | |
| "response": "def is_palindrome(s: str) -> bool:\n cleaned = ''.join(c.lower() for c in s if c.isalnum())\n return cleaned == cleaned[::-1]" | |
| }, | |
| { | |
| "instruction": "Write a JavaScript function to debounce a function call.", | |
| "response": "function debounce(fn, delay = 300) {\n let timer;\n return (...args) => {\n clearTimeout(timer);\n timer = setTimeout(() => fn(...args), delay);\n };\n}" | |
| }, | |
| { | |
| "instruction": "Write a Rust function that reads a file and returns lines as Vec<String>.", | |
| "response": "use std::fs::File;\nuse std::io::{BufRead, BufReader};\n\nfn read_lines(path: &str) -> Result<Vec<String>, std::io::Error> {\n let file = File::open(path)?;\n let reader = BufReader::new(file);\n reader.lines().collect()\n}" | |
| }, | |
| { | |
| "instruction": "Write a SQL query to find employees who earn more than their department's average.", | |
| "response": "SELECT e.name, e.salary, d.name AS department\nFROM employees e\nJOIN departments d ON e.department_id = d.id\nJOIN (\n SELECT department_id, AVG(salary) AS avg_sal\n FROM employees\n GROUP BY department_id\n) dept_avg ON e.department_id = dept_avg.department_id\nWHERE e.salary > dept_avg.avg_sal;" | |
| }, | |
| { | |
| "instruction": "Explain the CAP theorem and give examples of CP vs AP databases.", | |
| "response": "CAP Theorem: A distributed system can guarantee 2 of 3: Consistency, Availability, Partition Tolerance. Partitions are inevitable, so you choose CP or AP. CP databases: HBase, MongoDB, Redis cluster. AP databases: Cassandra, DynamoDB, CouchDB." | |
| }, | |
| { | |
| "instruction": "Write a Python async function that fetches multiple URLs concurrently.", | |
| "response": "import asyncio\nimport aiohttp\n\nasync def fetch_urls(urls, timeout=10.0):\n async with aiohttp.ClientSession() as session:\n async def fetch_one(url):\n async with session.get(url, timeout=aiohttp.ClientTimeout(total=timeout)) as resp:\n return await resp.text()\n tasks = [fetch_one(url) for url in urls]\n return await asyncio.gather(*tasks, return_exceptions=True)" | |
| }, | |
| { | |
| "instruction": "Implement an LRU cache in Python with O(1) get and put.", | |
| "response": "from collections import OrderedDict\n\nclass LRUCache:\n def __init__(self, capacity: int):\n self.cache = OrderedDict()\n self.capacity = capacity\n\n def get(self, key: int) -> int:\n if key not in self.cache:\n return -1\n self.cache.move_to_end(key)\n return self.cache[key]\n\n def put(self, key: int, value: int) -> None:\n if key in self.cache:\n self.cache.move_to_end(key)\n self.cache[key] = value\n if len(self.cache) > self.capacity:\n self.cache.popitem(last=False)" | |
| }, | |
| { | |
| "instruction": "Write a TypeScript type for a Redux reducer with discriminated union actions.", | |
| "response": "type Action =\n | { type: 'INCREMENT'; payload: number }\n | { type: 'DECREMENT'; payload: number }\n | { type: 'RESET' };\n\ninterface State { count: number; }\n\nfunction reducer(state: State, action: Action): State {\n switch (action.type) {\n case 'INCREMENT': return { count: state.count + action.payload };\n case 'DECREMENT': return { count: state.count - action.payload };\n case 'RESET': return { count: 0 };\n default: return state;\n }\n}" | |
| }, | |
| { | |
| "instruction": "Write a Go function to reverse a linked list.", | |
| "response": "type ListNode struct {\n Val int\n Next *ListNode\n}\n\nfunc reverseList(head *ListNode) *ListNode {\n var prev *ListNode\n curr := head\n for curr != nil {\n next := curr.Next\n curr.Next = prev\n prev = curr\n curr = next\n }\n return prev\n}" | |
| }, | |
| { | |
| "instruction": "What is the difference between inner join, left join, and full outer join?", | |
| "response": "INNER JOIN: Only matching rows from both tables. LEFT JOIN: All rows from left table, NULLs for non-matches on right. FULL OUTER JOIN: All rows from both tables, NULLs where no match exists on either side." | |
| } | |
| ] |