Instructions to use reallexi/lexi-coder-v4.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

lexi-coder-v4.4 / samples.json

reallexi

Publish Reallexi job #1996: Lexi Coder v4.4 v2 safe recovery

5ef5f75 verified 18 days ago

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	{
	"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 x2 + y2 <= 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": "+ y2)0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x2 + y2)**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 x2 + y2 <= D2: cnt += 1 print(cnt) # print(x, y) # print(np.sqrt(x2 + y2)) # print(np.sqrt(D2)) # print(np.sqrt(D2 - x2)) # print(np.sqrt(D2 - y2))"
	},
	{
	"prompt": "n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2",
	"output": "+ y2)0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x2 + y2)**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 >"
	}
	]
	}