Instructions to use RigCodeAI/Qwen3.6-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RigCodeAI/Qwen3.6-35B-A3B 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("RigCodeAI/Qwen3.6-35B-A3B")
config = load_config("RigCodeAI/Qwen3.6-35B-A3B")

# 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 RigCodeAI/Qwen3.6-35B-A3B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RigCodeAI/Qwen3.6-35B-A3B"

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": "RigCodeAI/Qwen3.6-35B-A3B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RigCodeAI/Qwen3.6-35B-A3B 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 "RigCodeAI/Qwen3.6-35B-A3B"

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 RigCodeAI/Qwen3.6-35B-A3B

Run Hermes

hermes

OpenClaw new

How to use RigCodeAI/Qwen3.6-35B-A3B with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RigCodeAI/Qwen3.6-35B-A3B"

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 "RigCodeAI/Qwen3.6-35B-A3B" \
  --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"

Qwen3.6-35B-A3B / README.md

adampatarino

Upload folder using huggingface_hub

a61cbe1 verified 19 days ago

preview code

Raw

History Blame Contribute Delete

3.3 kB

	---
	library_name: mlx
	base_model: deepreinforce-ai/Ornith-1.0-35B
	tags:
	- mlx
	- mlx-vlm
	- moe
	- multimodal
	- vision
	- coding
	- agentic
	pipeline_tag: image-text-to-text
	---

	# leonsarmiento/Ornith-1.0-35B-4bit-mlx

	This model was converted to MLX format from [`deepreinforce-ai/Ornith-1.0-35B`](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) using mixed 4/8-bit quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 4-bit, making this a full multimodal model.

	Ornith-1.0-35B is a 35B-parameter MoE (Mixture of Experts) model fine-tuned from Qwen3.5-35B-A3B by DeepReinforce AI, using a self-improving RL training framework that jointly optimizes scaffold and solution rollouts for agentic coding tasks. Despite 35B total parameters, only ~3B are activated per token. It features 256 experts (8 active per token + 1 shared expert), hybrid full + linear (Gated DeltaNet) attention, and a vision encoder.

	## Benchmark Highlights

	\| Benchmark \| Ornith-1.0-35B \| Qwen3.5-35B \| Qwen3.6-35B \|
	\|-----------\|---------------\|-------------\|-------------\|
	\| Terminal-Bench 2.1 (Terminus-2) \| 64.2 \| 41.4 \| 52.5 \|
	\| Terminal-Bench 2.1 (Claude Code) \| 62.8 \| 38.9 \| 49.2 \|
	\| SWE-bench Verified \| 75.6 \| 70 \| 73.4 \|
	\| SWE-bench Pro \| 50.4 \| 44.6 \| 49.5 \|
	\| SWE-bench Multilingual \| 69.3 \| 60.3 \| 67.2 \|
	\| NL2Repo \| 34.6 \| 20.5 \| 29.4 \|
	\| Claw-eval Avg \| 69.8 \| 65.4 \| 68.7 \|

	## Use with mlx

	```bash
	pip install -U mlx-vlm
	```

	```bash
	python -m mlx_vlm.generate --model leonsarmiento/Ornith-1.0-35B-4bit-mlx --max-tokens 256 --temperature 1.0 --top-p 1.0 --prompt "Hello"
	```

	## Mixed Quantization Strategy

	This model uses layer-aware mixed-bit quantization that allocates higher precision to sensitive layers and lower precision to bulk parameters, maximizing quality per gigabyte.

	\| Bit Depth \| Layers \| Rationale \|
	\|-----------\|--------\|-----------\|
	\| 8-bit \| `embed_tokens`, `lm_head`, router `gate`, `shared_expert_gate`, `shared_expert`, `self_attn` (full attention), `linear_attn` (DeltaNet) \| Every token passes through these — routing accuracy, shared representation, and sequence modeling are non-negotiable \|
	\| 4-bit \| `vision_tower`, `switch_mlp` (routed experts) \| Bulk of parameters, only 8 of 256 experts active per token — natural redundancy tolerates lower precision \|

	### Quantization Details

	\| Layer \| Bits \| Group Size \|
	\|-------\|------\|------------\|
	\| `embed_tokens` \| 8 \| 64 \|
	\| `lm_head` \| 8 \| 64 \|
	\| `mlp.gate` (router) \| 8 \| 64 \|
	\| `shared_expert_gate` \| 8 \| 64 \|
	\| `shared_expert` \| 8 \| 64 \|
	\| `self_attn` (full attention) \| 8 \| 64 \|
	\| `linear_attn` (DeltaNet) \| 8 \| 64 \|
	\| `vision_tower` \| 4 \| 64 \|
	\| `switch_mlp` (routed experts) \| 4 \| 64 \|
	\| Default fallback \| 8 \| 64 \|

	- Quantization type: Mixed 4/8-bit (multimodal, vision preserved)
	- Group size: 64
	- Method: Custom `quant_predicate` via `mlx_vlm`

	## Recommended Inference Parameters

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| `temperature` \| 1.0 \|
	\| `top_p` \| 1.0 \|
	\| `top_k` \| 40 \|
	\| `min_p` \| 0.01 \|
	\| `repeat_penalty` \| 1.05 \|

	> Note: Ornith-1.0-35B uses Temp 1.0 and Top_p 1.0 per the model's Terminal-Bench 2.1 benchmark recipe. This is a Qwen3.5-based model — `preserve_thinking` is not applicable.

	---
	library_name: mlx
	base_model: deepreinforce-ai/Ornith-1.0-35B
	tags:
	- mlx
	- mlx-vlm
	- moe
	- multimodal
	- vision
	- coding
	- agentic
	pipeline_tag: image-text-to-text
	---

	# leonsarmiento/Ornith-1.0-35B-4bit-mlx

	This model was converted to MLX format from [`deepreinforce-ai/Ornith-1.0-35B`](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) using mixed 4/8-bit quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 4-bit, making this a full multimodal model.

	Ornith-1.0-35B is a 35B-parameter MoE (Mixture of Experts) model fine-tuned from Qwen3.5-35B-A3B by DeepReinforce AI, using a self-improving RL training framework that jointly optimizes scaffold and solution rollouts for agentic coding tasks. Despite 35B total parameters, only ~3B are activated per token. It features 256 experts (8 active per token + 1 shared expert), hybrid full + linear (Gated DeltaNet) attention, and a vision encoder.

	## Benchmark Highlights

	\| Benchmark \| Ornith-1.0-35B \| Qwen3.5-35B \| Qwen3.6-35B \|
	\|-----------\|---------------\|-------------\|-------------\|
	\| Terminal-Bench 2.1 (Terminus-2) \| 64.2 \| 41.4 \| 52.5 \|
	\| Terminal-Bench 2.1 (Claude Code) \| 62.8 \| 38.9 \| 49.2 \|
	\| SWE-bench Verified \| 75.6 \| 70 \| 73.4 \|
	\| SWE-bench Pro \| 50.4 \| 44.6 \| 49.5 \|
	\| SWE-bench Multilingual \| 69.3 \| 60.3 \| 67.2 \|
	\| NL2Repo \| 34.6 \| 20.5 \| 29.4 \|
	\| Claw-eval Avg \| 69.8 \| 65.4 \| 68.7 \|

	## Use with mlx

	```bash
	pip install -U mlx-vlm
	```

	```bash
	python -m mlx_vlm.generate --model leonsarmiento/Ornith-1.0-35B-4bit-mlx --max-tokens 256 --temperature 1.0 --top-p 1.0 --prompt "Hello"
	```

	## Mixed Quantization Strategy

	This model uses layer-aware mixed-bit quantization that allocates higher precision to sensitive layers and lower precision to bulk parameters, maximizing quality per gigabyte.

	\| Bit Depth \| Layers \| Rationale \|
	\|-----------\|--------\|-----------\|
	\| 8-bit \| `embed_tokens`, `lm_head`, router `gate`, `shared_expert_gate`, `shared_expert`, `self_attn` (full attention), `linear_attn` (DeltaNet) \| Every token passes through these — routing accuracy, shared representation, and sequence modeling are non-negotiable \|
	\| 4-bit \| `vision_tower`, `switch_mlp` (routed experts) \| Bulk of parameters, only 8 of 256 experts active per token — natural redundancy tolerates lower precision \|

	### Quantization Details

	\| Layer \| Bits \| Group Size \|
	\|-------\|------\|------------\|
	\| `embed_tokens` \| 8 \| 64 \|
	\| `lm_head` \| 8 \| 64 \|
	\| `mlp.gate` (router) \| 8 \| 64 \|
	\| `shared_expert_gate` \| 8 \| 64 \|
	\| `shared_expert` \| 8 \| 64 \|
	\| `self_attn` (full attention) \| 8 \| 64 \|
	\| `linear_attn` (DeltaNet) \| 8 \| 64 \|
	\| `vision_tower` \| 4 \| 64 \|
	\| `switch_mlp` (routed experts) \| 4 \| 64 \|
	\| Default fallback \| 8 \| 64 \|

	- Quantization type: Mixed 4/8-bit (multimodal, vision preserved)
	- Group size: 64
	- Method: Custom `quant_predicate` via `mlx_vlm`

	## Recommended Inference Parameters

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| `temperature` \| 1.0 \|
	\| `top_p` \| 1.0 \|
	\| `top_k` \| 40 \|
	\| `min_p` \| 0.01 \|
	\| `repeat_penalty` \| 1.05 \|

	> Note: Ornith-1.0-35B uses Temp 1.0 and Top_p 1.0 per the model's Terminal-Bench 2.1 benchmark recipe. This is a Qwen3.5-based model — `preserve_thinking` is not applicable.