Text Generation
Transformers
Safetensors
GGUF
phi3
ai-model-builder
fine-tuned
lora
reallexi
conversational
custom_code
text-generation-inference
Instructions to use reallexi/lexi-coder-v4.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reallexi/lexi-coder-v4.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reallexi/lexi-coder-v4.3", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-coder-v4.3", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-coder-v4.3", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use reallexi/lexi-coder-v4.3 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 reallexi/lexi-coder-v4.3:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.3:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf reallexi/lexi-coder-v4.3:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.3:F16
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 reallexi/lexi-coder-v4.3:F16 # Run inference directly in the terminal: ./llama-cli -hf reallexi/lexi-coder-v4.3:F16
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 reallexi/lexi-coder-v4.3:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf reallexi/lexi-coder-v4.3:F16
Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.3:F16
- LM Studio
- Jan
- vLLM
How to use reallexi/lexi-coder-v4.3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reallexi/lexi-coder-v4.3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reallexi/lexi-coder-v4.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.3:F16
- SGLang
How to use reallexi/lexi-coder-v4.3 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 "reallexi/lexi-coder-v4.3" \ --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": "reallexi/lexi-coder-v4.3", "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 "reallexi/lexi-coder-v4.3" \ --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": "reallexi/lexi-coder-v4.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use reallexi/lexi-coder-v4.3 with Ollama:
ollama run hf.co/reallexi/lexi-coder-v4.3:F16
- Unsloth Studio
How to use reallexi/lexi-coder-v4.3 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 reallexi/lexi-coder-v4.3 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 reallexi/lexi-coder-v4.3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for reallexi/lexi-coder-v4.3 to start chatting
- Pi
How to use reallexi/lexi-coder-v4.3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.3:F16
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": "reallexi/lexi-coder-v4.3:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use reallexi/lexi-coder-v4.3 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.3:F16
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 "reallexi/lexi-coder-v4.3:F16" \ --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 reallexi/lexi-coder-v4.3 with Docker Model Runner:
docker model run hf.co/reallexi/lexi-coder-v4.3:F16
- Lemonade
How to use reallexi/lexi-coder-v4.3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reallexi/lexi-coder-v4.3:F16
Run and chat with the model
lemonade run user.lexi-coder-v4.3-F16
List all available models
lemonade list
- Hermes Agent
How to use reallexi/lexi-coder-v4.3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v4.3:F16
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 reallexi/lexi-coder-v4.3:F16
Run Hermes
hermes
- Atomic Chat
File size: 3,797 Bytes
781b563 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"prompts": [
"[{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'content':",
"[{'role': 'system', 'content': 'You are a senior game tools and engine architecture engineer. Design maintainable systems with clear boundaries and",
"[{'role': 'system', 'content': 'You are an expert JavaScript, Three.js, and browser game developer. Produce original, runnable, clean code and explain"
],
"before": [
{
"prompt": "[{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'content':",
"output": "'Create a testing strategy for level generation in a browser game.'}, {'role': 'assistant', 'content': 'Test game logic separately from rendering. Three.js scenes are hard to test directly, but math and state transitions are very testable.\\n\\nExample pure movement test:\\n\\n```js\\nexport function integratePosition(position, velocity, dt) {\\n return {\\n x"
},
{
"prompt": "[{'role': 'system', 'content': 'You are a senior game tools and engine architecture engineer. Design maintainable systems with clear boundaries and",
"output": "practical implementation steps.'}, {'role': 'user', 'content': 'Create enemy AI for a coin collector. The enemy should ambush, react to the player, and be readable.'}, {'role': 'assistant', 'content': 'Use a small finite-state machine first. It is easier to debug than a tangle of booleans.\\n\\nEnemy states:\\n- `idle"
},
{
"prompt": "[{'role': 'system', 'content': 'You are an expert JavaScript, Three.js, and browser game developer. Produce original, runnable, clean code and explain",
"output": "practical tradeoffs.'}, {'role': 'user', 'content': 'Create a Three.js scene featuring a energy pickup. Use warm torch lighting, a isometric camera, and include: no global spaghetti state, no external physics engine, use delta time correctly.'}, {'role': 'assistant', 'content': 'Below is a scene plan and core implementation. For a full HTML file, wrap"
}
],
"after": [
{
"prompt": "[{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'content':",
"output": "'Add mobile touch controls to a Three.js browser game without ruining desktop keyboard controls.'}, {'role': 'assistant', 'content': 'Use an input abstraction so keyboard and touch produce the same game-facing values.\\n\\n```js\\nconst input = {\\n moveX: 0,\\n moveY: 0,\\n actionPressed: false\\n};\\n\\nfunction"
},
{
"prompt": "[{'role': 'system', 'content': 'You are a senior game tools and engine architecture engineer. Design maintainable systems with clear boundaries and",
"output": "practical implementation steps.'}, {'role': 'user', 'content': 'Create enemy AI for a coin collector. The enemy should ambush, react to the player, and be readable.'}, {'role': 'assistant', 'content': 'Use a small finite-state machine first. It is easier to debug than a tangle of booleans.\\n\\nEnemy states:\\n- `idle"
},
{
"prompt": "[{'role': 'system', 'content': 'You are an expert JavaScript, Three.js, and browser game developer. Produce original, runnable, clean code and explain",
"output": "practical tradeoffs.'}, {'role': 'user', 'content': 'Create a Three.js scene featuring a water orb. Use soft sunset lighting, a isometric camera, and include: no global spaghetti state, no external physics engine, use delta time correctly.'}, {'role': 'assistant', 'content': 'Below is a scene plan and core implementation. For a full HTML file, wrap"
}
]
} |