test1

README.md

@@ -10,8 +10,6 @@ language:
 - zh
 - ar
 - ru
-base_model:
-  - HuggingFaceTB/SmolLM3-3B-Base
 ---
 
 
@@ -41,7 +39,7 @@ The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it
 ### Key features
 - Instruct model optimized for **hybrid reasoning**
 - **Fully open model**: open weights + full training details including public data mixture and training configs
-- **Long context:** Trained on 64k context and supports up to **128k tokens** using YARN extrapolation
+- **Long context:** Trained on 64k context and suppots up to **128k tokens** using YARN extrapolation
 - **Multilingual**: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese)
 
 For more details refer to our blog post: https://hf.co/blog/smollm3
@@ -198,7 +196,7 @@ text = tokenizer.apply_chat_template(
 )
 ```
 
-For local inference, you can use `llama.cpp`, `ONNX`, `MLX`, `MLC` and `ExecuTorch`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23)
+For local inference, you can use `llama.cpp`, `ONNX`, `MLX` and `MLC`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23)
 
 ### vLLM and SGLang
 
@@ -358,35 +356,13 @@ The model has also been trained on Arabic (standard), Chinese and Russian data,
 Here is an infographic with all the training details 
 - The datasets used for pretraining can be found in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) and those used in mid-training and post-training will be uploaded later 
 - The training and evaluation configs and code can be found in the [huggingface/smollm](https://github.com/huggingface/smollm) repository.
-- The training intermediate checkpoints (including the mid-training and SFT checkpoints) are available at [HuggingFaceTB/SmolLM3-3B-checkpoints](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints)
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png)
-
-### EU Summary of Public Content
-
-The EU AI Act requires all GPAI models to provide a Public Summary of Training Content according to a [given template](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models).
-You can find the summary for this model below, as well as in its [development Space](https://huggingface.co/spaces/hfmlsoc/smollm3-eu-data-transparency).
-
-<iframe
-	src="https://hfmlsoc-smollm3-eu-data-transparency.hf.space"
-	frameborder="0"
-	width="850"
-	height="350"
-></iframe>
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png)
 
 ## Limitations
 
 SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
 
-## License
-[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
-## Citation
-```bash
-@misc{bakouch2025smollm3,
-  title={{SmolLM3: smol, multilingual, long-context reasoner}},
-  author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas},
-  year={2025},
-  howpublished={\url{https://huggingface.co/blog/smollm3}}
-}
-```
+## License
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

config.json

@@ -100,7 +100,6 @@
   "rope_scaling": null,
   "rope_theta": 5000000.0,
   "sliding_window": null,
-  "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.54.0.dev0",
   "use_cache": false,