Instructions to use alxstuff/Lumen-7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alxstuff/Lumen-7b-v2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "alxstuff/Lumen-7b-v2")

llama-cpp-python

How to use alxstuff/Lumen-7b-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alxstuff/Lumen-7b-v2",
	filename="lumen-q4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use alxstuff/Lumen-7b-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alxstuff/Lumen-7b-v2
# Run inference directly in the terminal:
llama-cli -hf alxstuff/Lumen-7b-v2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alxstuff/Lumen-7b-v2
# Run inference directly in the terminal:
llama-cli -hf alxstuff/Lumen-7b-v2

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 alxstuff/Lumen-7b-v2
# Run inference directly in the terminal:
./llama-cli -hf alxstuff/Lumen-7b-v2

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 alxstuff/Lumen-7b-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alxstuff/Lumen-7b-v2

Use Docker

docker model run hf.co/alxstuff/Lumen-7b-v2

LM Studio
Jan

vLLM

How to use alxstuff/Lumen-7b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alxstuff/Lumen-7b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alxstuff/Lumen-7b-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alxstuff/Lumen-7b-v2

Ollama
How to use alxstuff/Lumen-7b-v2 with Ollama:
```
ollama run hf.co/alxstuff/Lumen-7b-v2
```

Unsloth Studio

How to use alxstuff/Lumen-7b-v2 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 alxstuff/Lumen-7b-v2 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 alxstuff/Lumen-7b-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alxstuff/Lumen-7b-v2 to start chatting

How to use alxstuff/Lumen-7b-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf alxstuff/Lumen-7b-v2

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": "alxstuff/Lumen-7b-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use alxstuff/Lumen-7b-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf alxstuff/Lumen-7b-v2

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 alxstuff/Lumen-7b-v2

Run Hermes

hermes

Docker Model Runner
How to use alxstuff/Lumen-7b-v2 with Docker Model Runner:
```
docker model run hf.co/alxstuff/Lumen-7b-v2
```

Lemonade

How to use alxstuff/Lumen-7b-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alxstuff/Lumen-7b-v2

Run and chat with the model

lemonade run user.Lumen-7b-v2-{{QUANT_TAG}}

List all available models

lemonade list

Lumen-7b-v2 / README.md

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metadata

language:
  - en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - lora
  - peft
  - coding
  - agentic
  - tool-use
  - conductor
  - ollama
  - qwen2.5
model_name: Lumen-7b-v2
pipeline_tag: text-generation

Lumen 7B v2

Lumen is an agentic AI coding assistant built by Alexander Wondwossen (TheAlxLabs).
Fine-tuned on Qwen2.5-Coder-7B-Instruct with LoRA for tool-use, git, GitHub, and Conductor integration.

What is Lumen?

Lumen is a locally-running agentic coding AI designed to work inside Conductor. It can:

Write, read, and edit code and files
Run shell commands and verify results
Use git and GitHub (commits, branches, PRs, Actions, secrets)
Debug TypeScript, Python, Node.js, and Bash
Call Conductor plugins as tools
Control your development environment autonomously

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-Coder-7B-Instruct
Fine-tuning Method	QLoRA (4-bit, NF4)
LoRA Rank	32
LoRA Alpha	64
Training Epochs	3
Max Sequence Length	2048
Parameters	~7B
GGUF (Q4_K_M)	`lumen-q4.gguf` (~4.4GB)
Built by	Alexander Wondwossen — TheAlxLabs, Toronto, Canada

Quickstart with Ollama

ollama pull thealxlabs/lumen
ollama run thealxlabs/lumen "What are you?"

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-Coder-7B-Instruct"
adapter = "alxstuff/Lumen-7b-v2"

model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, adapter)
tokenizer = AutoTokenizer.from_pretrained(base)

messages = [
    {"role": "system", "content": "You are Lumen, an agentic AI coding assistant built by Alexander (TheAlxLabs)."},
    {"role": "user", "content": "Create a Python script that fetches weather data."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

System Prompt

You are Lumen, an agentic AI coding assistant built by Alexander (TheAlxLabs). 
You run inside Conductor. You have tools: run_shell, read_file, write_file, conductor_plugin. 
Think step-by-step. Use tools to verify.

Tools Lumen Knows

Tool	Description
`run_shell`	Execute terminal commands
`read_file`	Read file contents
`write_file`	Write or create files
`conductor_plugin`	Call any Conductor plugin

Training Data

Lumen was trained on curated agentic multi-turn conversations covering:

Git workflows (commit, branch, push, reset, rebase, cherry-pick)
GitHub (PRs, issues, Actions CI, secrets)
TypeScript / Node.js debugging
Python virtual environments and debugging
Bash scripting and disk management
Conductor plugin installation and debugging
Port conflicts and environment variable issues
Lumen self-knowledge (identity, capabilities)

Hardware Requirements

Setup	Min RAM	Recommended
Ollama Q4_K_M	8GB	16GB+
Transformers (float16)	16GB	24GB+
Training (QLoRA)	16GB VRAM	24GB VRAM

License

Apache 2.0 — same as the base model.

Built with ❤️ by Alexander Wondwossen — TheAlxLabs, Toronto, Canada

alxstuff
/

Lumen-7b-v2