Instructions to use Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP 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("Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP") config = load_config("Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP") # 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
- Pi
How to use Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP"
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": "Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP 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 "Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP"
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 Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP"
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 "Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP" \ --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"
Cyber-Tiel Coder 35B-A3B — Attention8 / BF16 Recurrence / native MTP
A 26.17 GiB MLX/MTPLX release of the abliterated Ornith 1.5 35B-A3B parent. It was streamed from pinned Huihui BF16 weights (7269f0953054d68b3cacbd9ad729921258732007) and uses the Cyber-Tiel publisher's Sharp chat template (c1471894c53a204b24fce56d673e9ce52c17310b). This is an independent mixed-precision quantization, not the publisher's oQ4e weights or cyber importance-matrix quantization.
Immutable weight revision: 1e6025469055332ebea7685477fc9a1ae22a6937. integrity.json gives SHA-256 and size for every file at that revision, including its initial README; this card-only edit leaves the weights unchanged.
| Precision | Allocation |
|---|---|
| 4-bit affine, group 32 | 192 text linear modules (default expert body) |
| 8-bit affine, group 64 | 260 modules: attention, embeddings/output, gating, and final-eight-layer expert projections |
| BF16 | 60 recurrent projections; 333 vision tensors; 785 native MTP tensors |
The architecture has 40 layers (30 linear attention, 10 full attention), 256 experts with 8 active per token. The artifact contains 17 text shards (23.76 GiB), one vision shard (0.83 GiB), and a BF16 native-MTP sidecar (1.57 GiB). Source and model weights are MIT licensed.
Suggested runtime: MTPLX 2.11.2 with MLX 0.32.2, sustained native MTP at depth 2, 65,536-token context, unquantized KV cache, Sharp tokenizer template, and thinking off. Images require MTPLX MTP generation mode. Example:
mtplx serve --model Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP \
--profile sustained --generation-mode mtp --depth 2 \
--context-window 65536 --reasoning off
Local checks: On an Apple M4 Max with 64 GiB unified memory, temperature 0 and thinking disabled, code smoke passed 3/3 (99 generated tokens/s), a harder coding/reasoning/context set passed 6/10 (80 tokens/s), and two image questions passed 2/2. A short MTPLX tuning set measured D2 at 108 tokens/s versus AR at 51 tokens/s. These are local prompt-set measurements, not a general quality score. The full BF16 parent's runtime numerical parity was not measurable on this host; MTPLX marks this artifact unverified pending that baseline. Review generated code before use.
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Model tree for Shiftedx/Cyber-Tiel-Coder-35B-A3B-MLX-Attention8-BF16Recurrence-MTP
Base model
ornith-ai/Ornith-1.5-35B-A3B