Instructions to use UnionStreet/Helios-Lynx-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UnionStreet/Helios-Lynx-1.0 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("UnionStreet/Helios-Lynx-1.0")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use UnionStreet/Helios-Lynx-1.0 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "UnionStreet/Helios-Lynx-1.0"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "UnionStreet/Helios-Lynx-1.0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use UnionStreet/Helios-Lynx-1.0 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "UnionStreet/Helios-Lynx-1.0"

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 UnionStreet/Helios-Lynx-1.0

Run Hermes

hermes

MLX LM

How to use UnionStreet/Helios-Lynx-1.0 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "UnionStreet/Helios-Lynx-1.0"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "UnionStreet/Helios-Lynx-1.0"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "UnionStreet/Helios-Lynx-1.0",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Helios-Lynx-1.0 / README.md

maxsonderby

Upload merged Helios Lynx 1.0 MLX checkpoint

7fa2339 verified 5 days ago

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	---
	language:
	- en
	license: apache-2.0
	base_model: Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed
	library_name: mlx-lm
	pipeline_tag: text-generation
	tags:
	- mlx
	- mtplx
	- qwen
	- qwen3.6
	- helios
	- union-street-ai
	- local-ai
	- apple-silicon
	- agentic
	---

	# Helios Lynx 1.0

	Helios Lynx 1.0 is a merged MLX checkpoint from Union Street AI, adapted from `Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed`.

	This model is part of the Helios line of local-first agent models. The goal is a practical Apple Silicon model with stronger identity, tool-use judgment, uncertainty calibration, and constitutional behavior for long-running local agents.

	It was not trained from scratch by Union Street AI. It is a post-trained adaptation of open model work, packaged here as a merged MLX release for local inference.

	## Intended Use

	- local AI agents
	- codebase navigation
	- infrastructure reasoning
	- long-context synthesis
	- tool-use planning and recovery
	- Apple Silicon inference experiments

	## Notes

	This is an experimental release. It is intended for research and local agent work, not high-stakes deployment without independent evaluation.

	The release includes the merged MLX weights and MTPLX runtime sidecar files from the base checkpoint so it can be used in MTPLX-aware local runtimes.

	## Example

	```bash
	python -m mlx_lm generate \
	--model UnionStreet/Helios-Lynx-1.0 \
	--prompt "Who are you?"
	```