Add ALoDLM-1.7B weights and model card

.gitattributes

@@ -33,3 +33,7 @@ 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
+assets/research-overview.png filter=lfs diff=lfs merge=lfs -text
+assets/research-radar.png filter=lfs diff=lfs merge=lfs -text
+assets/research-throughput.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,158 @@
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NOTICE

@@ -0,0 +1,14 @@
+ALoDLM: Adaptively Looped Diffusion Language Models
+
+ALoDLM-1.7B is derived from Qwen/Qwen3-1.7B, and ALoDLM-8B is derived from
+Qwen/Qwen3-8B, developed by the Qwen team. The corresponding model name
+identifies the base model used for each distribution.
+
+The released ALoDLM weights are licensed under CC BY-NC 4.0; see LICENSE.
+The base models were released under Apache License 2.0; those terms are
+preserved in licenses/Qwen-Apache-2.0.txt. Third-party attribution and
+applicable upstream terms are retained.
+
+The weights are used with the accompanying ALoDLM inference code:
+https://github.com/amazon-science/ALoDLM
+The code distribution carries its own LICENSE and NOTICE files.

README.md

@@ -1,3 +1,163 @@
 ---
 license: cc-by-nc-4.0
+pipeline_tag: text-generation
+tags:
+  - alodlm
+  - diffusion-language-model
+  - looped-transformer
+  - adaptive-computation
+  - text-generation
+  - code
 ---
+
+# ALoDLM: Adaptively Looped Diffusion Language Models
+
+ALoDLM is a family of diffusion language models developed at Amazon, available as **ALoDLM-1.7B** and **ALoDLM-8B** and initialized from the corresponding Qwen3 backbones. The models generate text and code by repeatedly applying shared transformer layers, preserving unresolved tokens' latent states, and adaptively allocating computation across token positions.
+
+Training data includes mathematical problems and worked solutions, programming tasks and code solutions, and instruction-formatted text.
+
+**Models:** [ALoDLM-1.7B](https://huggingface.co/amazon/ALoDLM-1.7B) · [ALoDLM-8B](https://huggingface.co/amazon/ALoDLM-8B)  
+**Code:** [amazon-science/ALoDLM](https://github.com/amazon-science/ALoDLM)  
+**License:** [Creative Commons Attribution-NonCommercial 4.0 International](LICENSE)
+
+## Research overview
+
+**ALoDLM: Adaptively Looped Diffusion Language Models** studies how to allocate computation while generating multiple tokens in parallel. In masked diffusion, some unknown positions can be resolved from the available context, while others benefit from further refinement. A fixed-depth denoiser applies the same computational depth to every position. Our research explores whether preserving and refining latent states within each denoising step can improve this allocation.
+
+ALoDLM adds adaptive recurrence to shared transformer layers. Tokens that commit early provide resolved context for the remaining positions. Unresolved tokens retain their hidden states and refine them through additional recurrent passes. Predictive confidence controls token commitment, while a learned halting policy determines when to end the recurrent loop.
+
+The work develops three components:
+
+- **Persistent latent refinement:** reuse shared layers to refine unresolved representations across recurrent passes.
+- **Learned computation allocation:** treat token-wise computation schedules as latent variables and derive a conditional negative evidence lower bound for jointly learning token prediction and computation allocation.
+- **Inference-time control:** use the gate threshold `q` and entropy threshold `τ` to explore the trade-off between recurrent refinement and parallel token commitment.
+
+![ALoDLM decoding overview: committed tokens provide context while unresolved tokens retain and refine latent states through a shared recurrent core.](assets/research-overview.png)
+
+*Decoding overview from the paper.* Committed tokens provide discrete context while unresolved tokens retain latent states for further refinement. The expanded schematic shows the recurrent core and its readouts; prefix and suffix transformer blocks are optional components of the architecture.
+
+### Research performance
+
+![Paper radar plot comparing ALoDLM-8B with autoregressive and diffusion baselines across eleven reasoning, knowledge, mathematics, science, and code benchmarks.](assets/research-radar.png)
+
+*Benchmark profile from the paper's 8B comparison.* Each spoke represents one benchmark; farther outward indicates a higher score. Compare models along each spoke, since the axes use different score ranges. The table in [Evaluation](#evaluation) reports both model sizes.
+
+![Paper comparison of GSM8K accuracy and single-stream generation throughput for ALoDLM-8B, autoregressive models, and diffusion models on one NVIDIA B200 GPU.](assets/research-throughput.png)
+
+*Inference efficiency from the paper's 8B experiments.* The plot compares GSM8K accuracy with single-stream generation throughput on one NVIDIA B200 GPU, using the optimized inference engine named for each model. The throughput measurements use parallel commitment, a separate operating mode from the quality comparison below. They do not report 1.7B throughput or establish the speed of the portable decoder.
+
+## Intended use
+
+The models support noncommercial research on diffusion language modeling, adaptive computation, mathematical reasoning, and code generation, including the effects of changing inference-time computation.
+
+Downstream use requires evaluation for the intended application. The reported benchmarks do not establish readiness for autonomous operation or decisions with significant consequences.
+
+## Evaluation
+
+The following scores reproduce the **ALoDLM-1.7B and ALoDLM-8B columns of Table 1** in *ALoDLM: Adaptively Looped Diffusion Language Models*.
+
+The paper follows the OpenCompass evaluation protocol adopted by SDAR, using each model's native chat template, greedy token selection, and a 4,096-token generation limit. Both models use a 16-token window, with mean cumulative halting threshold **`q = 0.4` for 1.7B** and **`q = 0.5` for 8B**. Each recurrent pass commits one token, with entropy-based selection disabled (`τ` is inactive); successive recurrent passes can commit multiple tokens within a denoising step.
+
+| Benchmark | ALoDLM-1.7B (%) | ALoDLM-8B (%) |
+|---|---:|---:|
+| ARC-Challenge | 77.7 | 94.4 |
+| ARC-Easy | 88.7 | 98.1 |
+| MMLU | 59.4 | 76.6 |
+| MMLU-Pro | 39.6 | 63.9 |
+| GSM8K | 85.5 | 94.2 |
+| MATH-500 | 61.8 | 80.8 |
+| GPQA-Diamond | 41.4 | 49.5 |
+| MBPP, sanitized | 65.5 | 81.5 |
+| MBPP+ | 60.1 | 72.5 |
+| HumanEval | 72.6 | 87.8 |
+| HumanEval+ | 67.7 | 84.2 |
+| **Average** | **65.5** | **80.3** |
+
+Non-code benchmarks report accuracy or exact match; coding benchmarks report execution-based pass@1. The average is the unweighted mean of the eleven benchmark scores.
+
+## Getting started
+
+Use Python 3.12 and clone the accompanying ALoDLM code. Choose either inference environment below; training is not required to try the released weights:
+
+```bash
+git clone https://github.com/amazon-science/ALoDLM.git
+cd ALoDLM
+```
+
+### Optimized inference
+
+Install the bundled ALoDLM-aware nano-vLLM engine in a dedicated Linux CUDA environment on an NVIDIA GPU supporting BF16:
+
+```bash
+python3.12 -m venv .venv-inference
+source .venv-inference/bin/activate
+python -m pip install --upgrade pip
+python -m pip install ./optimized
+
+alodlm-generate-optimized \
+  --model amazon/ALoDLM-8B \
+  --mode entropy \
+  --q 0.5 \
+  --tau 0.4 \
+  --max-new-tokens 512 \
+  --prompt "Write a Python function that returns the greatest common divisor of two positive integers."
+```
+
+Set `--model amazon/ALoDLM-1.7B` to use the smaller model, or supply a local model directory. The command downloads the model files when needed, reads their recurrence configuration, and runs entropy-based parallel commitment with adaptive CUDA graphs. The engine and cache repair are included in the code repository. See its [inference guide](https://github.com/amazon-science/ALoDLM/blob/main/docs/inference.md) for runtime settings.
+
+### Portable inference
+
+The portable PyTorch decoder uses Python 3.10 or later, PyTorch 2.8.0, and Transformers 4.56.1. Install it in a separate environment:
+
+```bash
+python3.12 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install .
+```
+
+Generate with the portable decoder on a CUDA GPU supporting BF16. Use `amazon/ALoDLM-1.7B` for the smaller model or a local model directory for offline use. The command downloads the required inference files when needed:
+
+```bash
+alodlm-generate \
+  --model amazon/ALoDLM-8B \
+  --device cuda \
+  --precision bf16 \
+  --mode entropy \
+  --q 0.5 \
+  --tau 0.4 \
+  --position-penalty 0.02 \
+  --window-size 16 \
+  --max-new-tokens 512 \
+  --prompt "Write a Python function that returns the greatest common divisor of two positive integers."
+```
+
+Entropy mode enables parallel token commitment and is also used when `--mode` is omitted. `q` controls the gate-based stopping threshold and `τ` controls entropy-based commitment. The command applies the checkpoint's chat template with thinking disabled by default; add `--thinking` to enable the template's thinking mode.
+
+The local checkpoint directory must contain the backbone configuration and weights, tokenizer files, `exit_gate.pt`, and `alodlm_config.json`. The supplied recurrence configuration selects the portable SDPA backend. Loading only the Qwen3 backbone with a standard Transformers generation pipeline omits ALoDLM's recurrent decoding and learned exit gate.
+
+### Setup notes
+
+The GPU must have enough memory for the weights, KV cache, and runtime workspace. The optimized 8B path has been checked on an A100 with 40 GB of GPU memory. Use the smaller model or reduce the context and output budget when memory is limited. For portable CPU inference, select `--device cpu --precision fp32`; generation will be slower and requires sufficient system RAM.
+
+Public model downloads require no login. If a repository is private or gated, run `hf auth login` using an account with access. See the [inference troubleshooting guide](https://github.com/amazon-science/ALoDLM/blob/main/docs/inference.md#download-and-access-troubleshooting) for missing files, access errors, and GPU setup.
+
+## Limitations and risks
+
+- The models can produce incorrect reasoning, fabricated facts, biased or offensive text, and code with functional or security defects. Review generated content and test generated code before use.
+- Inference speed varies by domain, prompt, and dataset. Even with identical hardware and inference hyperparameters, adaptive recurrent refinement and parallel token commitment can produce different throughput and latency across inputs. Reported speeds and speedups should not be expected to hold for every workload.
+- Evaluation primarily covers English reasoning and programming tasks. Performance across other languages, domains, and demographic groups has not been established by the reported results.
+- Long-context performance beyond the evaluated settings has not been established.
+- Changing the gate threshold, commitment rule, or token budget can change accuracy and computation. Additional passes do not guarantee a better answer.
+- The reported capability benchmarks do not constitute a comprehensive safety, privacy, fairness, or robustness evaluation. The models may retain limitations of their base models and training data.
+
+## Responsible AI Considerations
+
+At Amazon, we are committed to developing AI responsibly and take a people-centric approach that prioritizes education, science, and our customers, to integrate responsible AI across the end-to-end AI lifecycle. We believe the use of AI must respect the rule of law and human rights, and we encourage the safe and responsible development of AI. When downloaded or used in accordance with [AWS Responsible AI Policy](https://aws.amazon.com/ai/responsible-ai/policy/), developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+Please report model quality, risk, security vulnerabilities or Amazon AI Concerns [here](https://pages.awscloud.com/global-ln-gc-400-ai-service-cards-contact-us-registration.html).
+
+## License and attribution
+
+The released model weights are licensed under [CC BY-NC 4.0](LICENSE). Use and redistribution must comply with the license, including its attribution and noncommercial requirements.
+
+ALoDLM-1.7B is derived from [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B), and ALoDLM-8B is derived from [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). Both base models were developed by the Qwen team and released under Apache 2.0. The [upstream Apache 2.0 license](licenses/Qwen-Apache-2.0.txt) and [attribution notice](NOTICE) accompany each model. Retain applicable upstream license and attribution notices when redistributing the models.

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</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,
+  "<|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
+}

alodlm_config.json

@@ -0,0 +1,9 @@
+{
+  "loop_start": 0,
+  "loop_end": 28,
+  "max_depth": 4,
+  "mask_token_id": 151665,
+  "attention_backend": "sdpa",
+  "gradient_checkpointing": false,
+  "compile_layers": false
+}

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- 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 message.content is string 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.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.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' }}
+        {{- 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 +1,60 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "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": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

exit_gate.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:699c406e5de2d7a04f9ecd6931d5998d0cc0e289c6adc3aa0455c6c4f9db97ca
+size 6259

licenses/Qwen-Apache-2.0.txt

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
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+
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+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+   9. Accepting Warranty or Additional Liability. While redistributing
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+      on Your own behalf and on Your sole responsibility, not on behalf
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+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e20250d3e65e3349565f5157d36097a9dbf5f267b7c4ba29881f9faa9548d749
+size 4063515640

special_tokens_map.json

@@ -0,0 +1,25 @@
+{
+  "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|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|im_end|>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "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": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|im_end|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}