Lucis

.eval_results/aime_2026.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: MathArena/aime_2026
-    task_id: MathArena/aime_2026
-  value: 97.1
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

.eval_results/gpqa.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: Idavidrein/gpqa
-    task_id: diamond
-  value: 87.2
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

.eval_results/hle.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: cais/hle
-    task_id: hle
-  value: 46
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

.eval_results/mmmu_pro.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: MMMU/MMMU_Pro
-    task_id: mmmu_pro_standard_10_options
-  value: 73.5
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

.eval_results/swe-bench_pro.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: ScaleAI/SWE-bench_Pro
-    task_id: SWE_Bench_Pro
-  value: 54.3
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

.eval_results/swe-bench_verified.yaml

@@ -1,7 +0,0 @@
-- dataset:
-    id: SWE-bench/SWE-bench_Verified
-    task_id: swe_bench_%_resolved
-  value: 77.6
-  source:
-    url: https://huggingface.co/thinkingmachines/Inkling
-    name: Model Card

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: apache-2.0 
 license_link: https://www.apache.org/licenses/LICENSE-2.0
 pipeline_tag: image-text-to-text
 tags:
@@ -7,7 +7,6 @@ tags:
 - image-text-to-text
 - audio-text-to-text
 - moe
-library_name: transformers
 ---
 
 # Inkling
@@ -17,8 +16,8 @@ library_name: transformers
 <p align="center">
   <a href="https://huggingface.co/thinkingmachines/Inkling">BF16</a> |
   <a href="https://huggingface.co/thinkingmachines/Inkling-NVFP4">NVFP4</a> |
-  <a href="https://tinker.thinkingmachines.ai/playground">Playground</a> |
   <a href="https://github.com/thinking-machines-lab/tinker-cookbook">Tinker Cookbook</a> |
+  <a href="https://tinker-docs.thinkingmachines.ai/cookbook/inkling/">Documentation</a> |
   <a href="https://thinkingmachines.ai/model-acceptable-use-policy">Acceptable Use</a>
 </p>
 
@@ -30,15 +29,15 @@ Inkling is a general-purpose multimodal model that accepts text, image and audio
 
 ## 2. Getting Started
 
-Try Inkling on the [Tinker Playground](https://tinker.thinkingmachines.ai/playground) or access via API using the [Tinker Cookbook](https://github.com/thinking-machines-lab/tinker-cookbook).
+For accessing Inkling via Tinker: You can get started by referring to the Tinker Cookbook [here](https://github.com/thinking-machines-lab/tinker-cookbook) and associated documentation [here](https://tinker-docs.thinkingmachines.ai/cookbook/inkling/).
 
 Inkling supports local deployment using the following open-source libraries:
 
-* SGLang ([recipe](https://docs.sglang.io/cookbook/autoregressive/ThinkingMachines/Inkling))
-* vLLM ([recipe](https://recipes.vllm.ai/thinkingmachines/Inkling))
-* TokenSpeed ([recipe](https://lightseek.org/tokenspeed/recipes/models#Inkling))
-* Unsloth ([recipe](https://unsloth.ai/docs/models/inkling))
-* Huggingface ([recipe](https://hf.co/blog/thinkingmachines-inkling))
+* SGLang ([recipe](https://docs.sglang.io/cookbook/autoregressive/ThinkingMachines/Inkling), [PR](https://github.com/sgl-project/sglang/pull/31358))
+* vLLM ([recipe](https://recipes.vllm.ai/thinkingmachines/inkling), [PR](https://github.com/vllm-project/vllm/pull/48768))
+* TokenSpeed ([recipe](https://lightseek.org/tokenspeed/recipes/models#Inkling), [PR](https://github.com/lightseekorg/tokenspeed/pull/689))
+* Unsloth ([recipe](https://unsloth.ai/docs/models/inkling), [PR](https://github.com/ggml-org/llama.cpp/pull/25731))
+* Huggingface ([recipe](https://hf.co/blog/thinkingmachines-inkling), [PR](https://github.com/huggingface/transformers/pull/47347))
 
 API access is also available through third party inference providers.
 
@@ -101,7 +100,7 @@ Inkling results are reported at effort=0.99. Comparison scores are generated Jul
 |     | IFBench | 79.8%   | 81.4%            | 70.2%     | 76.0%     | 73.3%   | 76.5%           | 77.1%                 | 63.5%                | 72.7%               |
 |     | Global-MMLU-Lite | 88.7%   | 85.6%            | 84.0%     | 88.4%     | 89.2%   | 89.3%           | 92.7%                 | 93.3%                | 91.8%               |
 | **Vision** |     |         |                  |           |           |         |                 |                       |                      |                     |
-|     | MMMU Pro (Standard 10) | 73.5%   | –                | 75.0%     | 79.0%     | –       | –               | 82.0%                 | 84.2%                | 83.0%               |
+|     | MMMU Pro (Standard 10) | 73.3%   | –                | 75.0%     | 79.0%     | –       | –               | 82.0%                 | 84.2%                | 83.0%               |
 |     | Charxiv RQ | 78.1%   | –                | 77.5%     | 80.4%     | –       | –               | 80.2%                 | 86.5%                | 84.7%               |
 |     | Charxiv RQ (with python) | 82.0%   | –                | 78.7%     | 86.7%     | –       | –               | 89.9%                 | 89.4%                | 87.8%               |
 | **Audio** |     |         |                  |           |           |         |                 |                       |                      |                     |