Simplify model card layout and evaluation; clarify license scope

LICENSE

@@ -1,52 +1,21 @@
-Kimi K3 License
+License scope
+=============
 
-Copyright (c) 2026 Moonshot AI
+Original contributions by the GroundingPI and GroundAnything authors are
+licensed under the Apache License, Version 2.0, provided in LICENSE-Apache-2.0.
+The project imposes no additional use or commercial restrictions on those
+contributions beyond that license.
 
-Permission is hereby granted, free of charge, to any person (the "Licensee")
-obtaining a copy of this software — including the model weights, parameters,
-configuration files, inference and training code, and associated documentation
-(collectively, the "Software") — to deal in the Software without restriction.
-This includes, without limitation, the rights to use, copy, modify, merge,
-publish, distribute, sublicense, and/or sell copies of the Software; to run,
-deploy, fine-tune, or otherwise modify the Software and create derivative works
-from it; and to permit persons to whom the Software is furnished to do so, in
-each case subject to the following conditions:
+This grant covers only rights held by the contributing authors. It does not
+relicense third-party material or waive any upstream requirements.
 
-1. The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software. Licensee's use of the
-Software must comply with applicable laws and regulations.
+Kimi-derived material remains subject to the Kimi K3 License, reproduced
+unchanged in LICENSE-Kimi-K3. Its conditions, including conditions that apply
+to derivative works, continue to apply to the combined model package where
+applicable. Apache 2.0 is not an alternative license for that material.
 
-2. "Model as a Service" means giving a third party access to language model
-inference or fine-tuning (e.g., via API) in a manner that allows such third
-party to exercise meaningful control over the inputs, parameters, or training
-data. This does not include (a) end-user products with model capabilities solely
-embedded within specific features or harnesses, or (b) mere relaying of requests
-to models hosted by others.
+Other third-party components retain their own licenses and copyright notices.
+Preserve the applicable license texts and notices when redistributing.
 
-If the Licensee or any of its affiliates operates a Model as a Service business,
-and the aggregate revenue of the Licensee and its affiliates exceeds 20 million
-US dollars (or the equivalent in other currencies) in total over any consecutive
-12 months, the Licensee must enter into a separate agreement with Moonshot AI
-before using the Software or its derivative works for any commercial purpose. 
-
-3. If the Software (or any derivative works thereof) is used for any of the
-Licensee's commercial products or services that have more than 100 million
-monthly active users, or more than 20 million US dollars (or equivalent in other
-currencies) in monthly revenue, "Kimi K3" must be prominently displayed on the
-user interface of such product or service.
-
-4. The requirements set forth in Sections 2 and 3 do not apply to: (a) internal
-use of the Software, defined as any use that does not make the Software, its
-outputs, or its underlying capabilities available to third parties; or (b) any
-use of the Software accessed through Moonshot AI's official products or
-certified inference partners.
-
-5. THE SOFTWARE AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”
-BASIS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
-LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE
-AND NONINFRINGEMENT. IN NO EVENT SHALL MOONSHOT AI OR ITS AFFILIATES OR
-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-For any questions regarding this license, please contact <license@moonshot.ai>.
+Upstream Kimi K3 license source:
+https://huggingface.co/moonshotai/Kimi-K3/blob/f831ab66814297da540d832a5235f8e904f29d06/LICENSE

LICENSE-Apache-2.0

@@ -0,0 +1,202 @@
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LICENSE-Kimi-K3

@@ -0,0 +1,52 @@
+Kimi K3 License
+
+Copyright (c) 2026 Moonshot AI
+
+Permission is hereby granted, free of charge, to any person (the "Licensee")
+obtaining a copy of this software — including the model weights, parameters,
+configuration files, inference and training code, and associated documentation
+(collectively, the "Software") — to deal in the Software without restriction.
+This includes, without limitation, the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of the Software; to run,
+deploy, fine-tune, or otherwise modify the Software and create derivative works
+from it; and to permit persons to whom the Software is furnished to do so, in
+each case subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software. Licensee's use of the
+Software must comply with applicable laws and regulations.
+
+2. "Model as a Service" means giving a third party access to language model
+inference or fine-tuning (e.g., via API) in a manner that allows such third
+party to exercise meaningful control over the inputs, parameters, or training
+data. This does not include (a) end-user products with model capabilities solely
+embedded within specific features or harnesses, or (b) mere relaying of requests
+to models hosted by others.
+
+If the Licensee or any of its affiliates operates a Model as a Service business,
+and the aggregate revenue of the Licensee and its affiliates exceeds 20 million
+US dollars (or the equivalent in other currencies) in total over any consecutive
+12 months, the Licensee must enter into a separate agreement with Moonshot AI
+before using the Software or its derivative works for any commercial purpose. 
+
+3. If the Software (or any derivative works thereof) is used for any of the
+Licensee's commercial products or services that have more than 100 million
+monthly active users, or more than 20 million US dollars (or equivalent in other
+currencies) in monthly revenue, "Kimi K3" must be prominently displayed on the
+user interface of such product or service.
+
+4. The requirements set forth in Sections 2 and 3 do not apply to: (a) internal
+use of the Software, defined as any use that does not make the Software, its
+outputs, or its underlying capabilities available to third parties; or (b) any
+use of the Software accessed through Moonshot AI's official products or
+certified inference partners.
+
+5. THE SOFTWARE AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”
+BASIS, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
+LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE
+AND NONINFRINGEMENT. IN NO EVENT SHALL MOONSHOT AI OR ITS AFFILIATES OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+For any questions regarding this license, please contact <license@moonshot.ai>.

README.md

@@ -1,7 +1,7 @@
 ---
 license: other
-license_name: kimi-k3
-license_link: https://huggingface.co/moonshotai/Kimi-K3/blob/f831ab66814297da540d832a5235f8e904f29d06/LICENSE
+license_name: apache-2.0-with-upstream-kimi-k3
+license_link: LICENSE
 language:
   - en
   - zh
@@ -18,20 +18,18 @@ tags:
 inference: false
 ---
 
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/logo.png" width="150" alt="GroundAnything logo" /></p>
-
-# GroundAnything-VLM: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
+# <img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/logo.png" width="40" style="display: inline-block; vertical-align: middle;" alt="GroundAnything logo" /> GroundAnything-VLM: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
 
 **Model family:** [GroundAnything — DLM / parallel decoding](https://huggingface.co/GroundingPI/GroundAnything) · [GroundAnything-VLM — autoregressive](https://huggingface.co/GroundingPI/GroundAnything-VLM).
 
 **This repository contains the autoregressive GroundAnything-VLM checkpoint.** The family overview and figures below are shared with the DLM page; use the VLM serving recipe for these weights.
 
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig1-teaser.png" width="100%" alt="GroundAnything Figure 1: broad visual grounding and parallel visual evidence extraction" /></p>
+<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig1-teaser.png" width="100%" alt="GroundAnything: broad visual grounding and parallel visual evidence extraction" /></p>
 
 ## 🔗 Quick Links
 
 - 🚀 **Online Demo:** Coming soon — XXX.
-- 💻 **GitHub Code:** Coming soon — XXX.
+- 💻 **GitHub Code:** [groundingpi/GroundAnything](https://github.com/groundingpi/GroundAnything).
 - 📄 **Paper:** [arXiv:2609.39600](https://arxiv.org/abs/2609.39600).
 - 🧪 **Evaluation data:** Coming soon — XXX.
 
@@ -49,13 +47,13 @@ An optional **self-speculative mode** achieves a **4.51× speedup** over the AR
 
 <video controls playsinline preload="none" width="100%" poster="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/demo-poster.jpg" src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/demo.mp4"></video>
 
-**Parallel decoding in action**
+**Parallel Decoding**
 
 <video controls playsinline preload="none" width="100%" src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/decoding.mp4"></video>
 
 ### License/Terms of Use:
 
-This checkpoint is released under the **Kimi K3 License**; see the repository's [LICENSE](LICENSE) and the [upstream license](https://huggingface.co/moonshotai/Kimi-K3/blob/f831ab66814297da540d832a5235f8e904f29d06/LICENSE). The model incorporates Kimi K3-derived vision components and implementation code. The license includes additional conditions for certain commercial uses. Third-party components retain their respective licenses and copyright notices.
+Our original contributions are available under **Apache 2.0**, with no additional restrictions imposed by this project. Third-party material retains its applicable licenses, including the **Kimi K3 License** for Kimi-derived material and applicable derivative works. Its conditions continue to apply when using or redistributing the combined model package. See [LICENSE](LICENSE) for the scope and full license texts.
 
 ### Deployment Geography:
 
@@ -105,13 +103,7 @@ Global.
 - **Spatial vocabulary:** 1,000 coordinate tokens shared with semantic labels and protocol markers.
 - **DLM conversion:** the shared decoder and vocabulary head support both causal prediction and bidirectional response-block denoising. A mask token is added for diffusion generation.
 
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig2-architecture.png" width="100%" alt="GroundAnything Figure 2: vision-language architecture and autoregressive-to-diffusion conversion" /></p>
-
-*Figure 2. Shared model architecture and the conversion to parallel grounding.*
-
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig4-attention-mask.png" width="640" alt="GroundAnything Figure 4: clean-stream causal and noisy-response block attention" /></p>
-
-*Figure 4. The attention mask used during diffusion conversion. The clean stream uses causal attention. A noisy response block attends bidirectionally within its block and reads the clean conditioning context and strictly preceding clean response blocks. Conversion uses B=32; the illustration uses two-token blocks.*
+<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig2-architecture.png" width="100%" alt="GroundAnything: vision-language architecture and autoregressive-to-diffusion conversion" /></p>
 
 ## Input(s):
 
@@ -158,49 +150,35 @@ The default DLM service uses **BF16, Triton attention, eager execution, one GPU,
 | [GroundAnything](https://huggingface.co/GroundingPI/GroundAnything) | Entropy-guided blockwise diffusion; optional self-speculation | **GAM** |
 | [GroundAnything-VLM](https://huggingface.co/GroundingPI/GroundAnything-VLM) | Autoregressive generation | **GAM** |
 
-The main GroundAnything benchmark results use **entropy-guided diffusion without autoregressive verification**. GroundAnything-VLM uses autoregressive decoding. The self-speculative speed–quality operating point is a separate experiment and should not be substituted for the main diffusion benchmark setting.
-
-## Testing and Evaluation Datasets:
-
-### Data Modality:
-
-Image and text, with task-specific box, point, text-region, or interaction annotations.
-
-## Evaluation Dataset:
+GroundAnything's main results use **entropy-guided decoding**; GroundAnything-VLM uses **autoregressive decoding**. Self-speculative decoding is an optional acceleration mode.
 
-**Evaluation data: XXX — download URL coming soon.**
+## Evaluation
 
-The shared evaluation toolkit provides **42 task recipes across eight task families**: Grounding, Referring, Dense, OCR, Layout, GUI, Pointing, and VisualPrompt. These are executable task/split recipes, not a count of distinct datasets. Dataset paths are registered in `configs/datasets.yaml`; task IDs are listed in `configs/eval/tasks.json`.
+The evaluation toolkit supports **7 modes**:
 
-### Evaluation Modes
-
-The evaluator accepts seven mode identifiers. A mode selects the **prompt and output parser** independently of the inference engine and decoding algorithm.
-
-| Mode | Supported evaluation interface | Output / coordinates |
-|:---|:---|:---|
-| **`GAM`** | **GroundingPI, GroundAnything, and GroundAnything-VLM** | Native spatial tokens, **0–999** |
-| `DLM` | Legacy compatible diffusion-checkpoint alias | Same GAM spatial-token protocol |
-| `RLV2` | Compatible RL-checkpoint alias | Same GAM spatial-token protocol |
-| `VLM` | Generic VLM baselines | Explicit pixel, 0–1000, or 0–1 coordinate mode |
-| `REXOMNI` | Rex-Omni adapter | Model-specific spatial-token parser |
-| `LOCATEANYTHING` | LocateAnything adapter | Its own output parser and generation-mode setting |
-| `GROUNDINGDINO` | External compatible GroundingDINO bridge | JSON coordinate responses |
+| Mode | Supported models |
+|:---|:---|
+| **`GAM`** | **GroundingPI, GroundAnything, GroundAnything-VLM** |
+| `VLM` | Generic vision-language baselines |
+| `REXOMNI` | Rex-Omni |
+| `LOCATEANYTHING` | LocateAnything |
+| `GROUNDINGDINO` | GroundingDINO through a compatible service |
+| `DLM` | Legacy diffusion checkpoints using the GAM protocol |
+| `RLV2` | Legacy RL checkpoints using the GAM protocol |
 
-**Use `mode: GAM` for all three of our checkpoints.** The `-VLM` suffix identifies an autoregressive checkpoint; it does not select the evaluator's generic `VLM` mode. The DLM's entropy-guided or self-speculative algorithm is selected separately through `decoder`.
+**All three released checkpoints use GAM mode.**
 
-External baseline weights and model services are separate from the evaluation toolkit. Metrics remain task-specific: box localization quality, point accuracy, OCR text-and-region matching, GUI grounding accuracy, and counting error. Full protocols and benchmark tables are provided in the paper and supplementary material.
+Evaluation code and instructions: [GitHub](https://github.com/groundingpi/GroundAnything).
 
 ## Quantitative Evaluation Benchmarks
 
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig7-grounding-performance.png" width="100%" alt="GroundAnything Figure 7: GroundAnything and GroundAnything-VLM benchmark overview" /></p>
-
-*Figure 7. Paper-reported capability overview. Detailed task-level scores, baseline settings, and evaluation protocols are provided in the paper and supplementary material.*
+<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig7-grounding-performance.png" width="100%" alt="GroundAnything: GroundAnything and GroundAnything-VLM benchmark overview" /></p>
 
 ## Inference:
 
 ### Installation
 
-Run the commands from the **GroundAnything source repository root** after obtaining the code package, using **Linux x86_64 and Python 3.12**. The code link above is reserved for the public release. Use the serving profile in the supplied source bundle so that the custom model adapter, decoding implementation, and dependencies remain aligned.
+Run the commands from the **GroundAnything source repository root** after obtaining the code package, using **Linux x86_64 and Python 3.12**. Use the serving profile in the supplied source bundle so that the custom model adapter, decoding implementation, and dependencies remain aligned.
 
 ```bash
 python3 -m pip install -r requirements.txt huggingface_hub
@@ -264,7 +242,7 @@ point_result = client.predict(
 )
 ```
 
-The HTTP client retains no model weights. A generic interactive request has no benchmark task identity; it does not automatically receive the benchmark's task-specific sampling and stopping policy. Use the evaluator below to reproduce the reported protocol.
+The HTTP client retains no model weights.
 
 ### Supported Tasks & Prompt Templates
 
@@ -299,64 +277,17 @@ After a block is complete, a causal forward reconstructs its authoritative KV ca
 
 The model uses its own shared weights to draft tokens with bidirectional attention and verify them with causal attention. Verification accepts the **longest consecutive matching prefix**, stops at the first mismatch, applies the causal correction, and discards the rejected suffix cache states. The shipped speculative route uses greedy verification; it is not a general stochastic speculative sampler.
 
-<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig6-self-speculative-decoding.png" width="100%" alt="GroundAnything Figure 6: linear and quadratic self-speculative schedules with shared model weights" /></p>
-
-*Figure 6. The paper studies two schedules: linear drafting and verification use two model forwards and 2B query tokens per round; quadratic fusion uses one forward after initialization with B(B + 1) query tokens. These counts describe queries and model calls, not total Transformer FLOPs.*
+<p align="center"><img src="https://huggingface.co/GroundingPI/GroundAnything-VLM/resolve/c367acf62cc8255782e3d19bd8c312f9b7ab35db/assets/fig6-self-speculative-decoding.png" width="100%" alt="GroundAnything: linear and quadratic self-speculative schedules with shared model weights" /></p>
 
 The documented `--decoder speculative` service is the linear shared-weight route. Exact greedy verification is relative to the converted model's causal branch; it does not imply identical outputs to the separately trained GroundAnything-VLM checkpoint.
 
-### Benchmark Sampling Policy
-
-Entropy-guided evaluation applies the five task profiles from `infer/decode/configs/task_profiles.json`:
-
-| Task profile | Recipes | Temperature | Top-p | Output-token budget |
-|:---|---:|---:|---:|---:|
-| Strict single target | 14 | 0 | 1 | 512 |
-| Medium, non-OCR | 12 | 0.1 | 0.95 | 4,096 |
-| Dense, non-OCR | 8 | 0.1 | 0.95 | 8,192 |
-| Medium OCR | 4 | 0.3 | 0.95 | 4,096 |
-| Dense OCR | 4 | 0 | 1 | 4,096 |
-
-The evaluator sends outer sampling parameters and the nested `custom_params.gam_dlm_decode` contract, adds the strict-single-target policy when required, and checks the server's actual decoder before sending requests. Unknown task profiles and mismatched decoders fail early. The entropy-guided benchmark policy rejects a global `max_tokens` override because it would replace the task budgets.
-
-Self-speculative evaluation uses greedy sampling (`temperature=0`, `top_p=1`, repetition penalty 1). GroundAnything-VLM retains its own autoregressive task recipes. All of these routes use **GAM** prompts and spatial-token parsing.
-
-### Run Evaluation
-
-```bash
-python3 run.py setup eval
-
-# Match the command to the service that is already running.
-python3 run.py eval --decoder denoise
-python3 run.py eval --decoder speculative
-python3 run.py eval --config configs/eval/vlm.yaml
-```
-
-Choose one evaluation command for the intended checkpoint/decoder:
-
-| Checkpoint / decoder | Recipe | Mode | Endpoint model ID |
-|:---|:---|:---|:---|
-| GroundAnything / entropy-guided | `configs/eval/dlm.yaml` | **GAM** | `groundinganything` |
-| GroundAnything / self-speculative | `configs/eval/dlm_speculative.yaml` | **GAM** | `groundinganything` |
-| GroundAnything-VLM / autoregressive | `configs/eval/vlm.yaml` | **GAM** | `groundinganything-vlm` |
-
-Use `service_contract: openai` and the corresponding port (8101 for DLM, 8102 for VLM). For a preflight that checks configured inputs without sending inference requests:
-
-```bash
-.venv-eval/bin/python scripts/evaluate.py configs/eval/dlm.yaml --dry-run
-```
-
-Configure the running service, `data_root`, dataset registry, task list, and a fresh `run_id` before launching. The default `limit: 8` is a smoke test; set **`limit: null`** for full evaluation. Evaluation connects to an existing service and does not start or change the decoder.
-
-Each run saves `run.json` (configuration and provenance), `responses.jsonl` (raw responses, finish reasons, and token usage), task logs, and `summary.json` (metrics and completion state). Record the checkpoint revision and task configuration when comparing results.
-
 ## Inference Infrastructure
 
 ### SGLang Execution
 
 The custom SGLang integration coordinates model loading, request scheduling, attention kernels, and KV-cache ownership for blockwise generation. Denoising uses bidirectional attention inside the active block; completed history is retained as causal KV. Self-speculation additionally verifies proposals and removes rejected suffix states. These cache semantics must be preserved when optimizing execution.
 
-The supplied recipe selects **BF16 + Triton attention + eager execution**. DLM recipes record the engine source, decoder, effective settings, profile digest, and package versions in `outputs/sglang/<decoder>/engine_runtime.json`; DLM evaluation checks decoder alignment before starting workers. The VLM service records its causal model/runtime configuration separately in `outputs/sglang/vlm/engine_runtime.json`. For higher service concurrency, use independent replicas with distinct devices, ports, and output directories; the default queue is not continuous multi-request batching.
+The supplied recipe selects **BF16 + Triton attention + eager execution**. DLM recipes record the engine source, decoder, effective settings, and package versions in `outputs/sglang/<decoder>/engine_runtime.json`. The VLM service records its causal model/runtime configuration separately in `outputs/sglang/vlm/engine_runtime.json`. For higher service concurrency, use independent replicas with distinct devices, ports, and output directories; the default queue is not continuous multi-request batching.
 
 ### CUDA Graph and Selective FP8
 

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