Text Generation
MLX
Safetensors
zaya
mixture-of-experts
hybrid-attention
cca-attention
apple-silicon
reasoning
tool-use
quantized
mxfp4
jang
osaurus
conversational
Instructions to use OsaurusAI/ZAYA1-8B-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/ZAYA1-8B-MXFP4 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("OsaurusAI/ZAYA1-8B-MXFP4") 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
- Pi
How to use OsaurusAI/ZAYA1-8B-MXFP4 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/ZAYA1-8B-MXFP4"
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": "OsaurusAI/ZAYA1-8B-MXFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use OsaurusAI/ZAYA1-8B-MXFP4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OsaurusAI/ZAYA1-8B-MXFP4"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/ZAYA1-8B-MXFP4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/ZAYA1-8B-MXFP4", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OsaurusAI/ZAYA1-8B-MXFP4 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 "OsaurusAI/ZAYA1-8B-MXFP4"
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 OsaurusAI/ZAYA1-8B-MXFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OsaurusAI/ZAYA1-8B-MXFP4 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/ZAYA1-8B-MXFP4"
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 "OsaurusAI/ZAYA1-8B-MXFP4" \ --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"
Initial upload: ZAYA1-8B-MXFP4 from Zyphra/ZAYA1-8B
Browse files- README.md +20 -20
- config.json +2 -2
- jang_config.json +2 -2
README.md
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| Source | [Zyphra/ZAYA1-8B](https://huggingface.co/Zyphra/ZAYA1-8B) |
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| License | Apache-2.0, inherited from upstream |
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| Format | MXFP4 |
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| Bundle size | 5.48 GiB |
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| Tensor keys | 1965 |
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| Expert layout | Pre-stacked `zaya_block.experts.switch_mlp` |
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| Runtime status | Generation coherence: NOT INDEPENDENTLY PASSED for the quantized runtime bundle (coherence report
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## Important Runtime Note
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This bundle requires a ZAYA-aware MLX/JANG runtime that implements CCA attention state and the converted pre-stacked expert layout.
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ZAYA is not a stock `mlx_lm` architecture. It alternates CCA attention layers
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## Architecture Summary
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- 80 decoder layers:
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- Hidden size 2048, 16 query heads, 2 KV heads, head dim
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- CCA state per attention layer: standard KV plus `conv_state [B,1280,2]`
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and `prev_hs [B,2048]`
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- 16 routed experts per MoE layer, top-1 routing with MOD skip route
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- Context length 131072, `rope_theta=5000000`
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Passthrough floor for first release prep:
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- `conv_qk.*`, `temp`, norms, residual scaling, router path, biases, and
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balancing biases are preserved as float tensors.
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- Embeddings and `lm_head` use 8-bit affine in the prepared bundles.
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- `jangtq_runtime.safetensors` is not applicable to MXFP4.
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`mxtq_bits`:
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- Converted bundles checked for `local_experts` removal.
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- Converted expert tensors checked for pre-stacked `switch_mlp` layout.
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- JANGTQ sidecars checked for the Swift runtime contract.
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- Runtime coherence status recorded above.
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## Runtime Smoke Tests
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Before production use, run short deterministic prompts through the exact target
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runtime:
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- `What is 2+2? Answer with only the number.`
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- `What is the capital of France? Answer with one word.`
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- One chat-template prompt with thinking disabled.
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- One chat-template prompt with thinking enabled and enough output budget for
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the final answer.
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The first public bundle release records bundle integrity and runtime contract
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checks. Full generation quality depends on a ZAYA-aware runtime implementation.
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## Korean Summary
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## Files
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- `config.json` carries `weight_format=mxfp4`
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`zaya_expert_layout=split_switch_mlp`.
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- `jang_config.json` carries `cache_subtype=zaya_cca`.
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- Tokenizer files and
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| Source | [Zyphra/ZAYA1-8B](https://huggingface.co/Zyphra/ZAYA1-8B) |
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| License | Apache-2.0, inherited from upstream |
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| Format | MXFP4 |
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| Modality | text |
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| Bundle size | 5.48 GiB |
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| Tensor keys | 1965 |
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| Expert layout | Pre-stacked `zaya_block.experts.switch_mlp` |
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| Runtime status | Generation coherence: NOT INDEPENDENTLY PASSED for the quantized runtime bundle (missing coherence report); published as a format/runtime bundle pending downstream ZAYA runtime validation. |
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## Important Runtime Note
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This bundle requires a ZAYA-aware MLX/JANG runtime that implements CCA attention state and the converted pre-stacked expert layout.
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ZAYA is not a stock `mlx_lm` architecture. It alternates CCA attention layers and top-1 MoE layers. Use this bundle only with a runtime that implements the ZAYA CCA state contract and the converted pre-stacked expert layout.
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## Runtime Pin Required
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Use a `vmlx-swift-lm` build that includes the ZAYA Swift runtime (`Libraries/MLXLLM/Models/Zaya.swift` + `MLXLMCommon/Cache/ZayaCCACache.swift` + `BatchEngine/BatchZayaCCACache.swift`). The first verified pin is commit `b9da180` or newer.
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## Architecture Summary
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- 80 decoder layers: alternating CCA attention and top-1 MoE
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- Hidden size 2048, 16 query heads, 2 KV heads, head dim ?
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- CCA state per attention layer: standard KV plus `conv_state [B,1280,2]` and `prev_hs [B,2048]`
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- 16 routed experts per MoE layer, top-1 routing with MOD skip route
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- Context length 131072, `rope_theta=5000000`
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Passthrough floor for first release prep:
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- `conv_qk.*`, `temp`, norms, residual scaling, router path, biases, and balancing biases are preserved as float tensors.
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- Embeddings and `lm_head` use 8-bit affine in the prepared bundles.
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- Text-only ZAYA1-8B has no vision_tower or LoRA tensors.
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- `jangtq_runtime.safetensors` is not applicable to MXFP4.
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`mxtq_bits`:
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- Converted bundles checked for `local_experts` removal.
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- Converted expert tensors checked for pre-stacked `switch_mlp` layout.
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- JANGTQ sidecars checked for the Swift runtime contract.
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- Capabilities verified: family=zaya, supports_thinking=False, tool_parser=zaya_xml.
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- Runtime coherence status recorded above.
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## Runtime Smoke Tests
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Before production use, run short deterministic prompts through the exact target runtime:
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- `What is 2+2? Answer with only the number.`
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- `What is the capital of France? Answer with one word.`
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- One chat-template prompt with thinking disabled.
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- One chat-template prompt with thinking enabled and enough output budget for the final answer.
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The first public bundle release records bundle integrity and runtime contract checks. Full generation quality depends on a ZAYA-aware runtime implementation.
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## Korean Summary
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## Files
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- `config.json` carries `weight_format=mxfp4`, `zaya_expert_layout=split_switch_mlp`.
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- `jang_config.json` carries `cache_subtype=zaya_cca`.
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- Tokenizer files and chat template are preserved from the upstream source snapshot.
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config.json
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"tool_parser": "zaya_xml",
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"think_in_template": true,
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"supports_tools": true,
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"supports_thinking":
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"family": "zaya",
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"modality": "text",
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"cache_type": "hybrid"
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}
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}
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"tool_parser": "zaya_xml",
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"think_in_template": true,
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"supports_tools": true,
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"supports_thinking": false,
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"family": "zaya",
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"modality": "text",
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"cache_type": "hybrid"
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}
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}
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jang_config.json
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"tool_parser": "zaya_xml",
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"think_in_template": true,
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"supports_tools": true,
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"supports_thinking":
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"family": "zaya",
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"modality": "text",
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"cache_type": "hybrid"
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}
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}
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"tool_parser": "zaya_xml",
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"think_in_template": true,
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"supports_tools": true,
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"supports_thinking": false,
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"family": "zaya",
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"modality": "text",
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"cache_type": "hybrid"
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}
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}
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