Add proper model card

README.md

@@ -1,7 +1,52 @@
 ---
-language: en
+license: mit
 library_name: mlx
 pipeline_tag: text-generation
+language:
+- en
+base_model: deepreinforce-ai/Ornith-1.0-397B
+base_model_relation: quantized
 tags:
 - mlx
+- qwen3_5_moe
+- moe
 ---
+
+# Ornith-1.0-397B-mlx-4bit
+
+This is an **MLX** conversion of [deepreinforce-ai/Ornith-1.0-397B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B), quantized to **4-bit** for use on Apple Silicon with [mlx-lm](https://github.com/ml-explore/mlx-lm).
+
+- **Base model:** [deepreinforce-ai/Ornith-1.0-397B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B) (Qwen3.5-MoE, `Qwen3_5MoeForConditionalGeneration`, 397B total / MoE)
+- **Format:** MLX, 4-bit (affine)
+- **Approx. size on disk:** ~223 GB
+- **Converted with:** mlx-lm 0.31.2
+
+> **Note — text-only.** The original Ornith-1.0-397B is multimodal (vision encoder + language model). mlx-lm converts the **language model only**; the vision tower is not included. This build is for **text generation**. The tokenizer, chat template, and `generation_config` are included.
+
+## Requirements
+
+This is a large MoE model. You need an Apple Silicon Mac with enough unified memory to hold the weights (roughly **~223 GB** plus runtime overhead/KV cache). A 512 GB M3 Ultra runs all of these comfortably.
+
+## Usage
+
+```bash
+pip install -U mlx-lm
+```
+
+```bash
+mlx_lm.generate --model pipenetwork/Ornith-1.0-397B-mlx-4bit \
+  --prompt "Write a haiku about Apple Silicon." --max-tokens 256
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("pipenetwork/Ornith-1.0-397B-mlx-4bit")
+messages = [{"role": "user", "content": "Explain mixture-of-experts in one paragraph."}]
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))
+```
+
+## License
+
+MIT, inherited from the base model.