Instructions to use oli-mebberson/lattice-spark-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oli-mebberson/lattice-spark-1.5b 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("oli-mebberson/lattice-spark-1.5b")

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 Settings
LM Studio

How to use oli-mebberson/lattice-spark-1.5b with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "oli-mebberson/lattice-spark-1.5b"

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": "oli-mebberson/lattice-spark-1.5b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use oli-mebberson/lattice-spark-1.5b with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "oli-mebberson/lattice-spark-1.5b"

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 "oli-mebberson/lattice-spark-1.5b" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use oli-mebberson/lattice-spark-1.5b with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "oli-mebberson/lattice-spark-1.5b"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "oli-mebberson/lattice-spark-1.5b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "oli-mebberson/lattice-spark-1.5b",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use oli-mebberson/lattice-spark-1.5b 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 "oli-mebberson/lattice-spark-1.5b"

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 oli-mebberson/lattice-spark-1.5b

Run Hermes

hermes

Lattice Spark 1.5B

A fine-tune of Qwen2.5-1.5B-Instruct with genuine Lattice identity ownership. Trained with MLX LoRA on Apple Silicon (Mac M4), fused into a standalone model.

What makes this different

Unlike a system prompt only rebrand, Spark genuinely knows it's Lattice Spark. The identity was baked into the weights via SFT — it says "Lattice Systems" with no system prompt (the base model says "Alibaba" / "Anthropic" when asked who made it).

Benchmark: Spark vs base (no system prompt)

Category	Base Qwen 1.5B	Spark 1.5B	Delta
Identity (/8)	0	7	+7
Factual (/8)	8	7	-1
Total (/16)	8	14	+6

Identity improved dramatically without breaking knowledge — the 1.5B base is big enough to absorb identity training without forgetting what it knows.

Training details

Base: mlx-community/Qwen2.5-1.5B-Instruct-4bit (dequantized for release)
Method: MLX LoRA (rank 8, alpha 16, dropout 0.05)
Data: 27 Lattice identity examples (3× weight) + 30 general Q&A = 111 total
Training: 50 iterations, LR 5e-5, ~35 seconds on M4
Trained parameters: 0.34% (5.3M / 1.54B)
Hardware: MacBook Air M4, 32GB

Usage

from mlx_lm import load, generate

model, tokenizer = load("oli-mebberson/lattice-spark-1.5b")
response = generate(model, tokenizer, prompt="Who are you?", max_tokens=80)
print(response)

Honest limitations

Small model (1.5B) — fluent but not deeply knowledgeable
Trained on 111 examples — identity is solid, general knowledge comes from the base model
No multi-turn optimisation
Occasionally generates trailing artifacts on some prompts