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: 2,923 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 | # Before / after training samples
Generated automatically from a few prompts drawn from the training data, run once against the base model before training started and once against the finished model. This shows what this run changed on representative prompts -- it is not a benchmark and does not measure generalization.
## Prompt: [{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'content':
**Before:**
> '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
**After:**
> '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
**Before:**
> 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
**After:**
> 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
**Before:**
> 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:**
> 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
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