fix todo

README.md

@@ -42,7 +42,7 @@ The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it
 - **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: TODO
+For more details refer to our blog post: https://hf.co/blog/smollm3
 
 ## How to use
 
@@ -181,7 +181,7 @@ text = tokenizer.apply_chat_template(
 )
 ```
 
-For local inference, you can use `llama.cpp`, `ONNX`, `MLX` and `MLC`. You can find quantized checkpoints in this collection [TODO].
+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
 
@@ -338,10 +338,11 @@ The model has also been trained on Arabic (standard), Chinese and Russian data,
 - **Post-training Framework:** [TRL](https://github.com/huggingface/trl)
 
 ### Open resources
-Here is an infographic with all the training details [TODO]. 
-- 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 pos-training can be found here [TODO]
+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.
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png)
 
 ## Limitations