Instructions to use Korzadi/Atomic-Code-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Korzadi/Atomic-Code-1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Korzadi/Atomic-Code-1.1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Korzadi/Atomic-Code-1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Korzadi/Atomic-Code-1.1 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 Korzadi/Atomic-Code-1.1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Korzadi/Atomic-Code-1.1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Korzadi/Atomic-Code-1.1:Q4_K_M # Run inference directly in the terminal: llama cli -hf Korzadi/Atomic-Code-1.1:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Korzadi/Atomic-Code-1.1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Korzadi/Atomic-Code-1.1:Q4_K_M
Use Docker
docker model run hf.co/Korzadi/Atomic-Code-1.1:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use Korzadi/Atomic-Code-1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Korzadi/Atomic-Code-1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Korzadi/Atomic-Code-1.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Korzadi/Atomic-Code-1.1:Q4_K_M
- SGLang
How to use Korzadi/Atomic-Code-1.1 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 "Korzadi/Atomic-Code-1.1" \ --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": "Korzadi/Atomic-Code-1.1", "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 "Korzadi/Atomic-Code-1.1" \ --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": "Korzadi/Atomic-Code-1.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Korzadi/Atomic-Code-1.1 with Ollama:
ollama run hf.co/Korzadi/Atomic-Code-1.1:Q4_K_M
- Unsloth Desktop
- Pi
How to use Korzadi/Atomic-Code-1.1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Korzadi/Atomic-Code-1.1:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Korzadi/Atomic-Code-1.1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Korzadi/Atomic-Code-1.1 with Docker Model Runner:
docker model run hf.co/Korzadi/Atomic-Code-1.1:Q4_K_M
- Lemonade
How to use Korzadi/Atomic-Code-1.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Korzadi/Atomic-Code-1.1:Q4_K_M
Run and chat with the model
lemonade run user.Atomic-Code-1.1-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Korzadi/Atomic-Code-1.1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Korzadi/Atomic-Code-1.1: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 Korzadi/Atomic-Code-1.1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Korzadi/Atomic-Code-1.1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Korzadi/Atomic-Code-1.1: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 "Korzadi/Atomic-Code-1.1: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"
a collab offer for the Atomic line: the loop around the code
saw Atomic-Code-1.1 on Coder-7B and AXYRON-ATOMIC-2 right after. code gen models learn the answer. my data adds the loop around it: 66 recorded sessions of an agent fixing real bugs in real repos, reads, edits, real commands, real errors, retries, tests green at the end. every session scored against the human fix, verify.py included so you can audit before training.
data built to make models better at coding, tool calling, agentic execution, and recovery from failure. your profile says open to collab, so here's a concrete one.
it's pretraining text, continued pretraining, not a fine-tune set.
I'm giving the first 100 away, so all 66 cost nothing:
https://github.com/Sumama-Jameel/megabit-trajectories
if you want it in the next Atomic run, I'll hand over whatever format helps.
Sumama