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
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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.
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Attention: GQA with 16 query heads / 8 KV heads, head_dim 128. Bidirectional self-attention with no causal mask.
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Context: 40,960 tokens max (RoPE, base theta 1e6).
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Params: 0.82B total with untied embeddings: embed_tokens 151,677 x 1024 and a separate lm_head of the same size.
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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.
Architecture graph
Use with Transformers
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))
Chat with it (chat.py)
python chat.py \
--model-path model.safetensors \
--tokenizer ./tokenizer \
--im-end-bias 2.0 --im-end-bias-t 0.3 --watch
Fine-tune (train.py)
# 1. Init: convert the AR model to a diffusion init
python convert.py --source Qwen/Qwen3-0.6B \
--output init/metadiffusion-600M-instruct.pt \
--tokenizer-out data/tokenizer
# 2. Corpus: smol, opc, math and no_robots, or a local --jsonl of {"messages": [...]} rows.
# --val-fraction holds out a disjoint val set for early stopping.
python prepare_data.py --datasets smol,math --out data \
--val-fraction 0.05
# 3. Train (defaults: lr 5e-5, bf16, seq 512, batch auto-detected)
python train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
--data-dir data --output-dir checkpoints --max-steps 30000
# 4. Continue a run: checkpoints carry model + optimizer + scheduler
# state, so --resume-from picks up LR position and momentum exactly
python train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
--data-dir data --output-dir checkpoints \
--resume-from checkpoints_p2/step_20000.pt --max-steps 16000
# 5. Test, then ship
python chat.py --model-path checkpoints_/step_30000.pt \
--tokenizer data/tokenizer --watch
python export_hf.py --checkpoint checkpoints/step_30000.pt \
--tokenizer data/tokenizer --output MetaDiffusion-600M-ChatBase
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