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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 |