Instructions to use reallexi/lexi-coder-v5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# 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=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-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

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-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/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-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

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 Studio

How to use reallexi/lexi-coder-v5.1 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-v5.1 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-v5.1 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-v5.1 to start chatting

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 @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-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"

lexi-coder-v5.1

lexi-coder-v5.1 by Reallexi LLC AI Model Builder — llm.reallexi.io

A standalone model of 3.86B parameters, derived from reallexi/lexi-coder-v4.3.

The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.

Size and requirements


Parameters	3,859,090,432 (3.86B)
Weights on disk	7.15 GB
Trained context length	1,024 tokens
Base model	`reallexi/lexi-coder-v4.3`

Approximate memory to hold the weights. Add context and runtime overhead on top.

Precision	Weights
FP16 / BF16	7.19 GB
8-bit (Q8_0)	3.59 GB
4-bit (Q4_K_M)	1.98 GB

Training


Strategy	lora
Adapter	Auto LoRA
LoRA rank / alpha	16 / 32
Dataset	`reallexi/lexi-coder-v3-datasest`
Samples learned	110,000 (through phase 28 of 258)
Training steps	15,000
Epochs	3

Before / after samples

The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in SAMPLES.md and samples.json alongside this file.

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…

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

Training curve

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lexi-coder-v5.1")
tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v5.1")

License and attribution

The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.

Base model: reallexi/lexi-coder-v4.3
Training data: reallexi/lexi-coder-v3-datasest

Produced by Reallexi LLC AI Model Builder from training job #161. Core: https://llm.reallexi.io

Who, where, and what platform trained this?

Produced by Reallexi LLC on Reallexi AI Model Builder, a local-first training platform (https://llm.reallexi.io). Hugging Face repository: reallexi/lexi-coder-v5.1. Copyright (c) 2026 Reallexi LLC. All rights reserved.

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Safetensors

Model size

4B params

Tensor type

F16

Model tree for reallexi/lexi-coder-v5.1

Base model

microsoft/Phi-4-mini-instruct

Adapter

reallexi/lexi-coder-v4.3

Adapter

(3)

this model

Adapters

2 models