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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,73 @@
+---
+license: apache-2.0
+base_model: dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1
+tags:
+  - diffusion-language-model
+  - block-diffusion
+  - bd3lm
+  - grpo
+  - rlvr
+  - lora
+  - qwen3
+language:
+  - en
+---
+
+# Qwen3-0.6B-diffusion-bd3lm-justgrpo-run1-gsm8k-lora
+
+**JustGRPO-style RL (LoRA)** applied to the tiny block-diffusion LM
+[`dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1`](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1).
+Trained on **GSM8K train split (7.4k grade-school math problems)** with verifiable rewards.
+
+- Method & code: [https://github.com/LLM-OS-Models/JustGRPO](https://github.com/LLM-OS-Models/JustGRPO)
+- Papers this work builds on:
+  [JustGRPO (arXiv:2601.15165)](https://arxiv.org/abs/2601.15165) ·
+  [dLLM (arXiv:2602.22661)](https://arxiv.org/abs/2602.22661) ·
+  [BD3LM (arXiv:2503.09573)](https://arxiv.org/abs/2503.09573)
+
+## How it was trained
+
+- Rollouts: native block-diffusion sampling (block_size 32, temperature 1.0,
+  256 denoising steps, gen length 256) — AR-order rollout collapses on this base
+  model, see the repo's ADAPTATION.md for the full analysis.
+- Loss: exact autoregressive log-likelihood of each sampled token, computed in a
+  single forward pass via the BD3LM `[x0 || xt]` concat-attention trick
+  (mathematically identical to the per-token loop; verified to 2e-5 in fp32),
+  weighted by GRPO group-normalized advantages with PPO-style clipping.
+- LoRA r=128, alpha=64, dropout 0.05 on q/k/v/o/up/down/gate projections,
+  lr 5e-5, 200 steps, 8 prompts x 16 rollouts per step, 1x H100.
+- Rewards: +1 correct / -1 incorrect (final-answer match)
+
+## Results
+
+Evaluation in progress — see the GitHub repo's BENCHMARKS.md for the live table.
+
+## Usage
+
+This repo contains the merged full model (base + LoRA). Generation uses block diffusion (NOT vanilla
+`model.generate`); the easiest path is the [dllm](https://github.com/ZHZisZZ/dllm)
+sampler:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from dllm.core.samplers import BD3LMSampler, BD3LMSamplerConfig
+
+tok = AutoTokenizer.from_pretrained("LLM-OS-Models2/Qwen3-0.6B-diffusion-bd3lm-justgrpo-run1-gsm8k-lora")
+model = AutoModelForMaskedLM.from_pretrained(
+    "LLM-OS-Models2/Qwen3-0.6B-diffusion-bd3lm-justgrpo-run1-gsm8k-lora", trust_remote_code=True, torch_dtype=torch.bfloat16
+).cuda().eval()
+
+sampler = BD3LMSampler(model=model, tokenizer=tok)
+prompt = tok.apply_chat_template(
+    [[{"role": "user", "content": "Natalia sold clips to 48 friends in April, "
+       "then half as many in May. How many altogether?"}]],
+    add_generation_prompt=True, tokenize=True)
+seqs = sampler.sample(prompt, config=BD3LMSamplerConfig(
+    max_new_tokens=256, steps=256, block_size=32, temperature=0.0))
+print(tok.decode(seqs[0], skip_special_tokens=True))
+```
+
+To evaluate: use the dllm eval harness
+(`--model a2d_bd3lm`, `max_new_tokens=256,steps=256,block_size=32`) as described in
+[https://github.com/LLM-OS-Models/JustGRPO](https://github.com/LLM-OS-Models/JustGRPO).

added_tokens.json

@@ -0,0 +1,29 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|mask|>": 151669,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,66 @@
+{
+  "architectures": [
+    "A2DQwen3LMHeadModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "modeling_qwen3.A2DQwen3Config",
+    "AutoModel": "modeling_qwen3.A2DQwen3Model",
+    "AutoModelForMaskedLM": "modeling_qwen3.A2DQwen3LMHeadModel"
+  },
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "a2d-qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": [
+    151645
+  ],
+  "pad_token_id": 151643,
+  "transformers_version": "4.57.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b8a9ee6d5dd04de5100757bebf2cd637bb7caee3adc2998070ddfb8983b4faa
+size 1192135096

modeling_qwen3.py

@@ -0,0 +1,172 @@
+from typing import Optional
+
+import torch
+from torch import nn
+
+import transformers
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+
+if transformers.utils.is_torch_flex_attn_available():
+    from torch.nn.attention.flex_attention import _DEFAULT_SPARSE_BLOCK_SIZE as flex_default_block_size
+    from torch.nn.attention.flex_attention import BlockMask, create_block_mask
+else:
+    # Register a fake type to avoid crashing for annotations and `isinstance` checks
+    BlockMask = torch.Tensor
+
+class A2DQwen3Config(transformers.Qwen3Config):
+    model_type = "a2d-qwen3"  # <- NEW model_type
+
+
+class A2DQwen3Model(transformers.Qwen3Model):
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        """
+        # -------------------------------------------------------------
+        # ORIGINAL CODE (causal mask)
+        # -------------------------------------------------------------
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "input_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            # Create the masks
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+            # The sliding window alternating layers are not always activated depending on the config
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+        # -------------------------------------------------------------
+        # ORIGINAL CODE (causal mask)
+        # -------------------------------------------------------------
+        """
+        # -------------------------------------------------------------
+        # NEW CODE (bidirectional, padding-only mask)
+        # -------------------------------------------------------------
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            batch_size, seq_len = inputs_embeds.shape[:2]
+            device = inputs_embeds.device
+
+            # 1) If no mask is provided → treat all tokens as valid (no padding)
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    batch_size, seq_len, device=device, dtype=torch.long
+                )
+
+            # 2) If mask is not already a 4D attention mask → convert it
+            if not (
+                isinstance(attention_mask, BlockMask)
+                or (isinstance(attention_mask, torch.Tensor) and attention_mask.ndim == 4)
+            ):
+                attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+            # 3) Build causal mask mapping used by the attention layers
+            causal_mask_mapping = {"full_attention": attention_mask}
+
+            # Sliding-window layers share the same non-causal mask
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = attention_mask
+        # -------------------------------------------------------------
+        # NEW CODE (bidirectional, padding-only mask)
+        # -------------------------------------------------------------
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+        )
+
+
+class A2DQwen3LMHeadModel(transformers.Qwen3ForCausalLM):
+    config: A2DQwen3Config
+
+    def __init__(self, config):
+        transformers.Qwen3PreTrainedModel.__init__(self, config)
+        self.model = A2DQwen3Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+
+transformers.AutoConfig.register("a2d-qwen3", A2DQwen3Config)
+transformers.AutoModel.register(A2DQwen3Config, A2DQwen3LMHeadModel)
+transformers.AutoModelForMaskedLM.register(A2DQwen3Config, A2DQwen3LMHeadModel)
+
+
+if __name__ == "__main__":
+    import dllm
+    import torch
+    from transformers import AutoModel
+
+    # Load a config from a local path (either a directory containing config.json, or the file itself)
+    config_path = dllm.utils.resolve_with_base_env(
+        "Qwen/Qwen3-0.6B-Base", "BASE_MODELS_DIR"
+    )
+    config = A2DQwen3Config.from_pretrained(config_path)
+    if hasattr(config, "auto_map"):
+        delattr(config, "auto_map")
+    if hasattr(config, "architectures"):
+        delattr(config, "architectures")
+
+    torch.set_default_device("cuda")
+    model = A2DQwen3LMHeadModel(config)
+    model.save_pretrained("models-tmp/a2d-qwen3")
+    auto_model = AutoModel.from_pretrained("models-tmp/a2d-qwen3")
+

special_tokens_map.json

@@ -0,0 +1,45 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|mask|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d55810de1daf81d1d1259355b4b85810385022ab745be68029295935a4eacf0
+size 11422839

tokenizer_config.json

@@ -0,0 +1,249 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
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+    "<|object_ref_start|>",
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}