Instructions to use reallexi/lexi-coder-v5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reallexi/lexi-coder-v5.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reallexi/lexi-coder-v5.1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-coder-v5.1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-coder-v5.1", 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=256) 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-v5.1 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 reallexi/lexi-coder-v5.1:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v5.1: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-v5.1:F16 # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v5.1:F16
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 reallexi/lexi-coder-v5.1:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf reallexi/lexi-coder-v5.1:F16
Use Docker
docker model run hf.co/reallexi/lexi-coder-v5.1:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use reallexi/lexi-coder-v5.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reallexi/lexi-coder-v5.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reallexi/lexi-coder-v5.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reallexi/lexi-coder-v5.1:F16
- SGLang
How to use reallexi/lexi-coder-v5.1 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 "reallexi/lexi-coder-v5.1" \ --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": "reallexi/lexi-coder-v5.1", "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 "reallexi/lexi-coder-v5.1" \ --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": "reallexi/lexi-coder-v5.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use reallexi/lexi-coder-v5.1 with Ollama:
ollama run hf.co/reallexi/lexi-coder-v5.1:F16
- Unsloth Desktop
- Pi
How to use reallexi/lexi-coder-v5.1 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-v5.1:F16
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": "reallexi/lexi-coder-v5.1:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use reallexi/lexi-coder-v5.1 with Docker Model Runner:
docker model run hf.co/reallexi/lexi-coder-v5.1:F16
- Lemonade
How to use reallexi/lexi-coder-v5.1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reallexi/lexi-coder-v5.1:F16
Run and chat with the model
lemonade run user.lexi-coder-v5.1-F16
List all available models
lemonade list
- Hermes Agent
How to use reallexi/lexi-coder-v5.1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reallexi/lexi-coder-v5.1: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-v5.1:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use reallexi/lexi-coder-v5.1 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-v5.1: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-v5.1: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"
Download SAMPLES.md from reallexi/lexi-coder-v5.1: direct link, hf CLI and curl.
- Browser
- Download file 2.55 kB
-
https://huggingface.co/reallexi/lexi-coder-v5.1/resolve/main/SAMPLES.md
- Command line
-
hf download hf://reallexi/lexi-coder-v5.1/SAMPLES.md
-
curl -L -o SAMPLES.md https://huggingface.co/reallexi/lexi-coder-v5.1/resolve/main/SAMPLES.md
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: def partition(p, r): i = p for j in range(p, r): if A[r][1] >= A[j][1]: A[i], A[j] = A[j], A[i]
Before:
i += 1 return partition(p, r) if partition(p, r) == partition(p, r): return partition(p, r) else: return partition(p, r)
n = int(input()) A = [tuple(map(int, input().split())) for _ in range(n)] print(*partition(0, n)[1:]) for i in range(n) if partition(0,
After:
i += 1 return i def quicksort(p, r): if p < r: i = partition(p, r) quicksort(p, i-1) quicksort(i, r) def mergeSort(p, r): if p < r: q = partition(p, r) mergeSort(p, q) mergeSort(q, r) def printList(): print(" ", end =
Prompt: {i : Node(None, None, None) for i in range(n)} # 情報を入れるdictをNodeクラスで作成 for _ in range(n): # 変数名を「 _ 」にすることによって、「その変数を使っていない」ことを表現している(Pythonの習慣) tmp
Before:
= input().split() # 変数名と値を入れる tmp[0] = tmp[0].strip() # 変数名を入れる tmp[1] = tmp[1].strip() # 値を入れる tmp[2] = tmp[2].strip() # 値を入れる tmp[3] = tmp[3].strip() #
After:
= input().split() # 入れられた値を変数に入れる tmp[0] = int(tmp[0]) # 変数の値をintに変換 tmp[1] = int(tmp[1]) # 変数の値をintに変換 tmp[2] = int(tmp[2]) # 変数の値をintに変
Prompt: rmat(node_id), end = '') _pre_walk(self.nodes[node_id].left_child) _pre_walk(self.nodes[node_id].right_child) _pre_walk(self.root_id) print('') def inorder_walk(self): print('Inorder') root_node = self.nodes[self.root_id] def _in_walk(node_id): if node_id != -1:
Before:
_in_walk(self.nodes[node_id].left_child) print(str(self.nodes[node_id].value)) _in_walk(self.nodes[node_id].right_child) def _post_walk(self, node_id, end = ''): if node_id != -1: _post_walk(self.nodes[node_id].left_child) print(str(self.nodes[node_id].value)) _post_walk(self.nodes[node_id].right
After:
print(' ', end = '') print(node_id, end = '') _in_walk(self.nodes[node_id].left_child) _in_walk(self.nodes[node_id].right_child) _in_walk(self.nodes[node_id].parent) def post_walk(self): print('Post') root_node = self.nodes[self.root_id] def _post_walk(node_id): if node_id != -1: _post_walk