Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- HuggingFaceTB/smol-smoltalk
+- HuggingFaceH4/no_robots
+- nvidia/OpenMathInstruct-2
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- metadiffusion
+- diffusion
+- diffusion-lm
+- ar-to-diffusion
 ---
+
+# MetaDiffusion-600M-ChatBase
+
+Experimental bidirectional masked-diffusion chat model converted from Qwen3-0.6B via AR-to-diffusion model surgery (28L x 1024W, ~0.82B params, untied head, bf16, 40K-token context (RoPE base 1e6), Apache-2.0). Intended as a base for further SFT, not a production chatbot.
+
+## What this is
+
+The AR checkpoint becomes the initialization (weights copied, timestep modules zero-init, the [MASK] and seven auxiliary "rainbow" padding rows are mean-initialized); diffusion behavior is learned throughout training. Trained using smol-smoltalk, no_robots, and OpenMathInstruct-2.
+
+## Architecture
+- Blocks: 28 transformer layers, hidden dim 1024, SwiGLU MLP with intermediate 3072, pre-norm RMSNorm (eps 1e-6), QK-norm on. Timestep conditioning is a sinusoidal MLP embedding (1024) feeding per-block adaLN-style scale+shift modulation.
+
+- Attention: GQA with 16 query heads / 8 KV heads, head_dim 128. Bidirectional self-attention with no causal mask.
+
+- Context: 40,960 tokens max (RoPE, base theta 1e6).
+
+- Params: 0.82B total with untied embeddings: embed_tokens 151,677 x 1024 and a separate lm_head of the same size.
+
+- Vocab / IO: 151,677 rows = Qwen3's 151,669 + [MASK] (id 151669) + 7 rainbow padding tokens (151670-151676); pad_token_id is <|endoftext|> (151643), eos is <|im_end|> (151645). bf16 weights, 371 tensors in model.safetensors.
+
+## Use with Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "CodeSoft/MetaDiffusion-600M-ChatBase"
+
+m = AutoModelForCausalLM.from_pretrained(
+    repo,
+    trust_remote_code=True,
+    dtype=torch.bfloat16,
+).to("cuda")
+
+tok = AutoTokenizer.from_pretrained(
+    repo,
+    subfolder="tokenizer",
+    trust_remote_code=True,
+)
+
+prompt = tok.apply_chat_template(
+    [{"role": "user", "content": "hi"}],
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tok(prompt, return_tensors="pt").to("cuda")
+
+with torch.inference_mode():
+    out = m.generate(
+        **inputs,
+        max_new_tokens=100,
+    )
+
+print(tok.decode(out[0], skip_special_tokens=True))
+
+```
+
+## Limitations
+
+This model is an experimental research checkpoint intended for further fine-tuning and experimentation. It is not optimized for instruction-following, factuality, safety, or production deployment. Behavior may differ substantially from the original Qwen3-0.6B-Instruct model.
+
+## License
+
+Apache-2.0