Instructions to use leonsarmiento/Occamy-1.0-6bit-XL-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonsarmiento/Occamy-1.0-6bit-XL-mlx with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("leonsarmiento/Occamy-1.0-6bit-XL-mlx")
config = load_config("leonsarmiento/Occamy-1.0-6bit-XL-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use leonsarmiento/Occamy-1.0-6bit-XL-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "leonsarmiento/Occamy-1.0-6bit-XL-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "leonsarmiento/Occamy-1.0-6bit-XL-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use leonsarmiento/Occamy-1.0-6bit-XL-mlx 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 "leonsarmiento/Occamy-1.0-6bit-XL-mlx"

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 leonsarmiento/Occamy-1.0-6bit-XL-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use leonsarmiento/Occamy-1.0-6bit-XL-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "leonsarmiento/Occamy-1.0-6bit-XL-mlx"

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 "leonsarmiento/Occamy-1.0-6bit-XL-mlx" \
  --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"

leonsarmiento/Occamy-1.0-6bit-XL-mlx

Occamy-1.0 by Accio-Lab — an agentic co-worker built on Qwen3.6-35B-A3B, quantized for Apple Silicon using the BaseQuant_XL 6/8-bit recipe.

Occamy-1.0 is purpose-built for long-horizon, stateful agent work: coordinated use of search, code, tools, files, structured APIs, and productivity software — with reliable execution, persistent state tracking, recovery, and follow-through. Best among 35B-A3B peers on most agentic benchmarks: 82.20 Claw-Eval avg (+12.7 over its Qwen3.6 base), 49.16 WildClawBench, 59.0 Terminal-Bench 2.1, 65.40 BFCL v4, 91.53 IFEval.

This is a full multimodal build — the vision tower is preserved. (Accio froze the vision encoder/projector during post-training, so vision behavior matches the Qwen3.6 base.)

About XL Quantization

BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.

Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.

Quickstart

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Occamy-1.0-6bit-XL-mlx --max-tokens 4096 --temperature 1.0 --top-p 0.95 --prompt "Search the repo for where errors from the payment API are swallowed, then propose a fix."

Works with LM Studio — vision mmproj included. Thinking mode is on by default (emits <think>...</think>).

Quantization Strategy

BaseQuant_XL recipe — precision is allocated by layer importance, not applied uniformly:

Layers	Bits	Rationale
`mlp.gate` (router), `shared_expert_gate`, `lm_head`, `shared_expert`	bf16	Routing decisions and output projection — any quantization noise here causes expert misrouting or output degradation
`embed_tokens`, `self_attn`, `linear_attn`	8-bit	Every-token layers — near-lossless, attention quality preserved
`vision_tower`, `switch_mlp` (routed experts)	6-bit	Bulk parameters — 256 experts with only 8 active per token; redundancy absorbs quantization noise. 6-bit is the sweet spot for routed experts (higher bits can cause overthinking)

Bits per weight: 6.808 · Total size: 29.9 GB · Group size: 64

Notes specific to this build:

Source stores routed experts as individual per-expert tensors (experts.N.{gate,up,down}_proj, N = 0–255) — stock mlx_vlm 0.7.0 sanitize stacks them into switch_mlp 3D tensors at load; no custom sanitize needed.
Source ships the vision tower as a separate model-visual.safetensors (transformers 5.x split layout) — picked up natively by the loader.
No MTP layer in the source (mtp_num_hidden_layers: 0).

Recommended Inference Parameters

Per the source model card:

Parameter	Value
`temperature`	1.0
`top_p`	0.95
`top_k`	20
`presence_penalty`	1.5
`max_tokens`	32,768
`reasoning_parser`	qwen3
`tool_call_parser`	qwen3_coder

Thinking is on by default (<think>...</think> before the answer); with a reasoning parser enabled the chain-of-thought is returned in a separate reasoning_content field. Context: 262,144 native (SFT-trained at 131,072).

Model Overview

Property	Value
Architecture	Qwen3.6-family MoE (35B-A3B) + native vision encoder
Parameters	35B total / ~3B active per token
Experts	256 (8 routed + 1 shared)
Attention	Hybrid — 30 linear_attn + 10 full attention (40 layers)
Modalities	text, image, video → text
Context window	262,144 tokens native
Thinking	`<think>...</think>` — on by default, `preserve_thinking` supported
Tool calling	XML-style (`<tool_call><function=...><parameter=...>`)
License	Apache 2.0

Source Model Benchmarks (from Accio-Lab)

Benchmark	Occamy-1.0
Claw-Eval (avg)	82.20
WildClawBench	49.16
AutomationBench Pass@1	27.60
Terminal-Bench 2.1	59.00
BFCL v4	65.40
IFEval	91.53

Trained openly via Accio's Dressage framework: full-parameter SFT (~15,000 trajectories / 403.3M tokens), HDPO, a two-expert merge (Marathon for sustained execution + Sprint for broad agentic capability), and SAO. Weights, dataset (occamy-data-1.0), and training code are all released.