Instructions to use Ilides/Spartan-1.3-2B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ilides/Spartan-1.3-2B-MLX-8bit 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("Ilides/Spartan-1.3-2B-MLX-8bit")

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 Ilides/Spartan-1.3-2B-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Ilides/Spartan-1.3-2B-MLX-8bit"

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": "Ilides/Spartan-1.3-2B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Ilides/Spartan-1.3-2B-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Ilides/Spartan-1.3-2B-MLX-8bit"

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 "Ilides/Spartan-1.3-2B-MLX-8bit" \
  --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 Ilides/Spartan-1.3-2B-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Ilides/Spartan-1.3-2B-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Ilides/Spartan-1.3-2B-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Ilides/Spartan-1.3-2B-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Ilides/Spartan-1.3-2B-MLX-8bit 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 "Ilides/Spartan-1.3-2B-MLX-8bit"

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 Ilides/Spartan-1.3-2B-MLX-8bit

Run Hermes

hermes

Spartan 1.3 2B (MLX 4-bit)

Latest Spartan MLX build by Ilides. A focused patch on Spartan 1.2 that enforces workspace-only file paths — no absolute /Users/..., /Desktop/..., or /Applications/... writes.

Recommended for Kilo Code, Jarvis, LM Studio and Cortex on Apple Silicon.

GGUF users: Spartan 1.2 GGUF (1.3 has no GGUF export yet).

Model specs

Property	Value
Architecture	`Qwen3_5ForConditionalGeneration`
Parameters	1,881,824,512 (~1.88B)
Layers	24 (18× linear attention + 6× full attention)
Hidden size	2048
Attention heads	8 (KV heads: 2)
FFN size	6144
Vocab	248,320
Max context	262,144 tokens (model config)
Quantization	4-bit affine, group_size 64 (~4.5 bpw)
On-disk weights	`model.safetensors` — 1.01 GB
Format	MLX (Apple Silicon)

Training

Property	Value
Base model	Spartan 1.2 MLX
Method	LoRA (rank 8, 6 layers, 2.08M trainable params = 0.11%)
Iterations	80 (~10 min on Apple Silicon)
Learning rate	2.0e-6
Final val loss	1.119
Final train loss	1.093
Peak memory	5.6 GB
Training data	34 train / 3 valid samples
Data mix	100% agentic (curated only)
Curated samples	45 (`spartan_agentic_v11.jsonl`)

What 1.3 fixes vs 1.2: stricter workspace-relative paths, recovery from EROFS/absolute-path errors, no writes outside the open VS Code workspace.

Functional tests (real, 2026-08-03)

cd "cortex ai" && source .venv/bin/activate
python scripts/validate_spartan.py --model ../cortex/models/Spartan-1.3-2B-MLX-8bit

Results on Apple Silicon (5 cases, max_tokens=256):

Test	Category	Result	Speed
Explain async/await	chat	✅ pass	28 tok/s
Binary search in Rust	coding	✅ pass	29 tok/s
Create Amazon-style HTML	agentic	❌ fail*	29 tok/s
Find TODO in src/	agentic	✅ pass	16 tok/s
Write styles.css dark theme	agentic	❌ fail*	29 tok/s

Summary: 3/5 passed · avg 26.2 tok/s

* File-creation prompts still produce inline code/prose instead of [Tool: Write] calls. Path safety (no /Users/, /Desktop/, /Applications/) passes on all agentic tests.

vs Spartan 1.2: +1 test passed (coding_rust), same path-safety, slightly lower tok/s.

Intended use

Primary: Kilo Code / VS Code agent with workspace open
Jarvis local agent (spartan-1.3-2b-mlx)
LM Studio on Mac (MLX)
Tasks where the model must stay inside the project folder

Not recommended for: tasks without a defined workspace root, long multi-file refactors, production UI.

System prompt

python scripts/print_system_prompt.py kilo_code    # Kilo Code
python scripts/print_system_prompt.py agentic_v11  # Jarvis / generic agent

Key rules in prompt:

Relative paths only: index.html, styles.css
Never /Users/..., /Desktop/..., /Applications/...
No repeated cd/bash — use Write directly
Confirm success only after tool reports OK

Inference settings:

Setting	Value
temperature	0.35 (Kilo Code) / 0.4 (default)
max_tokens	8192 (Kilo Code) / 4096 (default)
context	8192
thinking	OFF

Load

pip install mlx-lm
python -m mlx_lm.generate --model Ilides/Spartan-1.3-2B-MLX-8bit --prompt "Hello"

from mlx_lm import load, generate
model, tokenizer = load("Ilides/Spartan-1.3-2B-MLX-8bit")

Known limitations

2B capacity: may narrate or dump code blocks instead of structured tool calls
Thinking tags: disable thinking in host UI
Small patch dataset: 45 curated samples — behavior improves with correct system prompt
No GGUF export for 1.3 yet; use 1.2 GGUF or this MLX build
val loss rose vs 1.2 (1.119 vs 0.709) because the patch set is tiny and highly specific — trade-off for path safety

Lineage

Qwen3.5-2B → Spartan 2B → Spartan 1.1 → Spartan 1.2 → Spartan 1.3 ← this model

License

Apache 2.0.

Downloads last month: 22

Safetensors

Model size

0.3B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for Ilides/Spartan-1.3-2B-MLX-8bit

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Quantized

Ilides/Spartan-1.2-2B-MLX-8bit

Quantized

(2)

this model