Instructions to use reallexi/lexi-resume-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reallexi/lexi-resume-v6 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="reallexi/lexi-resume-v6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-resume-v6")
model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-resume-v6", 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-resume-v6 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-resume-v6:F16
# Run inference directly in the terminal:
llama cli -hf reallexi/lexi-resume-v6:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reallexi/lexi-resume-v6:F16
# Run inference directly in the terminal:
llama cli -hf reallexi/lexi-resume-v6: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-resume-v6:F16
# Run inference directly in the terminal:
./llama-cli -hf reallexi/lexi-resume-v6: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-resume-v6:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reallexi/lexi-resume-v6:F16

Use Docker

docker model run hf.co/reallexi/lexi-resume-v6:F16

LM Studio
Jan

vLLM

How to use reallexi/lexi-resume-v6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reallexi/lexi-resume-v6"
# 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-resume-v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reallexi/lexi-resume-v6:F16

SGLang

How to use reallexi/lexi-resume-v6 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-resume-v6" \
    --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-resume-v6",
		"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-resume-v6" \
        --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-resume-v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use reallexi/lexi-resume-v6 with Ollama:
```
ollama run hf.co/reallexi/lexi-resume-v6:F16
```

Unsloth Studio

How to use reallexi/lexi-resume-v6 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-resume-v6 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-resume-v6 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-resume-v6 to start chatting

How to use reallexi/lexi-resume-v6 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-resume-v6: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-resume-v6:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use reallexi/lexi-resume-v6 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-resume-v6: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-resume-v6: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-resume-v6 with Docker Model Runner:
```
docker model run hf.co/reallexi/lexi-resume-v6:F16
```

Lemonade

How to use reallexi/lexi-resume-v6 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reallexi/lexi-resume-v6:F16

Run and chat with the model

lemonade run user.lexi-resume-v6-F16

List all available models

lemonade list

Hermes Agent

How to use reallexi/lexi-resume-v6 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-resume-v6: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-resume-v6:F16

Run Hermes

hermes

Atomic Chat

lexi-resume-v6

lexi-resume-v6 by Reallexi LLC AI Model Builder — llm.reallexi.io

A standalone model of 495M parameters, derived from Qwen/Qwen2.5-0.5B-Instruct.

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

Size and requirements


Parameters	495,114,112 (495M)
Weights on disk	942 MB
Trained context length	8,192 tokens
Base model	`Qwen/Qwen2.5-0.5B-Instruct`

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

Precision	Weights
FP16 / BF16	944 MB
8-bit (Q8_0)	472 MB
4-bit (Q4_K_M)	260 MB

Training


Strategy	slm
Adapter	Auto LoRA
LoRA rank / alpha	8 / 16
Dataset	`AzharAli05/Resume-Screening-Dataset`
Samples learned	10,000 (through phase 3 of 3)
Training steps	2,250
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: Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact

Before: Information:

Address: 123 Main St, Anytown, USA 12345
Phone: (555) 555-5555
Email: mary.johnson@email.com Professional Summary: Highly motivated and experienced AR/VR Developer with expertise in Unity, C#,

After: Information:

Address: 123 Main St, Anytown, USA 12345
Phone: (555) 555-5555
Email: mary.johnson@email.com
LinkedIn: linkedin.com/in/maryjohnsondeveloper

Summary: Highly motivated and

Prompt: Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown

Before: Product Manager Contact Information:

Email: brent.brown@email.com
Phone: (123) 456-7890
LinkedIn: linkedin.com/in/brentbrown

Summary: Highly motivated and detail-oriented Product Manager with 5+ years of experience in driving successful product launches, delivering high

After: product manager

contact information:

email: brent.brown@email.com
phone: 555-555-5555
linkedin: linkedin.com/in/brentbrown

summary: highly motivated and detail-oriented product manager with 3+ years of experience in creating and executing successful product strategies.

Prompt: Role: data engineer; Resume: gina mehta data engineer candidate gina mehta is a highly skilled and experienced data engineer with

Before: over 5 years of experience in designing, developing, and deploying scalable data solutions. She has a strong background in cloud computing, database design, and data visualization.

Key skills:

Cloud platforms (AWS, Azure, Google Cloud)
Database management (MySQL, PostgreSQL, MongoDB)
Data modeling and query…

After: a strong background in designing, developing, and deploying scalable data solutions. she has a proven track record of delivering high-quality data products that meet the needs of clients across various industries.

key skills and achievements:

data engineering: demonstrated expertise in designing, developing…

Training curve

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lexi-resume-v6")
tokenizer = AutoTokenizer.from_pretrained("lexi-resume-v6")

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: Qwen/Qwen2.5-0.5B-Instruct
Training data: AzharAli05/Resume-Screening-Dataset

Produced by Reallexi LLC AI Model Builder from training job #1609. 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-resume-v6. Copyright (c) 2026 Reallexi LLC. All rights reserved.

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Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for reallexi/lexi-resume-v6

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(264)

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