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-v4.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reallexi/lexi-coder-v4.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reallexi/lexi-coder-v4.4", 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-v4.4", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-coder-v4.4", 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-v4.4 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-v4.4:Q4_K_M # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.4:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf reallexi/lexi-coder-v4.4:Q4_K_M # Run inference directly in the terminal: llama cli -hf reallexi/lexi-coder-v4.4:Q4_K_M
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-v4.4:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf reallexi/lexi-coder-v4.4:Q4_K_M
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-v4.4:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf reallexi/lexi-coder-v4.4:Q4_K_M
Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.4:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use reallexi/lexi-coder-v4.4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reallexi/lexi-coder-v4.4" # 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-v4.4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reallexi/lexi-coder-v4.4:Q4_K_M
- SGLang
How to use reallexi/lexi-coder-v4.4 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-v4.4" \ --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-v4.4", "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-v4.4" \ --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-v4.4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use reallexi/lexi-coder-v4.4 with Ollama:
ollama run hf.co/reallexi/lexi-coder-v4.4:Q4_K_M
- Unsloth Desktop
- Pi
How to use reallexi/lexi-coder-v4.4 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-v4.4: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": "reallexi/lexi-coder-v4.4:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use reallexi/lexi-coder-v4.4 with Docker Model Runner:
docker model run hf.co/reallexi/lexi-coder-v4.4:Q4_K_M
- Lemonade
How to use reallexi/lexi-coder-v4.4 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reallexi/lexi-coder-v4.4:Q4_K_M
Run and chat with the model
lemonade run user.lexi-coder-v4.4-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use reallexi/lexi-coder-v4.4 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-v4.4: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 reallexi/lexi-coder-v4.4:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use reallexi/lexi-coder-v4.4 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-v4.4: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 "reallexi/lexi-coder-v4.4: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"
File size: 2,628 Bytes
a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 5ef5f75 a9d0d03 | 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": [
"import numpy as np N, D = map(int, input().split()) cnt = 0 for i in range(N): x, y = map(int,",
"n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2",
"#n, m = map(int,input().split()) k = int(input()) #hl = list(map(int,input().split())) s = len(str(k)) for i in range(1,k+1): if int('7'*i) %"
],
"before": [
{
"prompt": "import numpy as np N, D = map(int, input().split()) cnt = 0 for i in range(N): x, y = map(int,",
"output": "input().split()) if x**2 + y**2 <= D**2: cnt += 1 print(cnt) # print(N, D) # print(N, D, cnt) # print(N, D, cnt, cnt) # print(N, D, cnt, cnt, cnt) # print(N, D, cnt, cnt, cnt, cnt)"
},
{
"prompt": "n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2",
"output": "+ y**2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x**2 + y**2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for"
},
{
"prompt": "#n, m = map(int,input().split()) k = int(input()) #hl = list(map(int,input().split())) s = len(str(k)) for i in range(1,k+1): if int('7'*i) %",
"output": "k == 0: print(i) break\nk = int(input())\nn = 7\nfor i in range(1,k+1):\n if n % k == 0:\n print(i)\n break\n n = n*10+7\nelse:\n print(-1) #k % 7 == 0\u306e\u6642\u306f-1\u3092\u8fd4\u3059"
}
],
"after": [
{
"prompt": "import numpy as np N, D = map(int, input().split()) cnt = 0 for i in range(N): x, y = map(int,",
"output": "input().split()) if x**2 + y**2 <= D**2: cnt += 1 print(cnt) # print(x, y) # print(np.sqrt(x**2 + y**2)) # print(np.sqrt(D**2)) # print(np.sqrt(D**2 - x**2)) # print(np.sqrt(D**2 - y**2))"
},
{
"prompt": "n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2",
"output": "+ y**2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x**2 + y**2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for"
},
{
"prompt": "#n, m = map(int,input().split()) k = int(input()) #hl = list(map(int,input().split())) s = len(str(k)) for i in range(1,k+1): if int('7'*i) %",
"output": "k == 0: print(i) break\nk = int(input())\nif k % 2 == 0 or k % 5 == 0:\n print(-1)\nelse:\n i = 1\n while True:\n if int('7'*i) % k == 0:\n print(i)\n break\n i += 1\n if i >"
}
]
} |