Update README.md

README.md

@@ -44,7 +44,7 @@ The model is a decoder-only transformer using GQA and NoPE, it was pretrained on
 - **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
 The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend.
@@ -65,7 +65,7 @@ outputs = model.generate(inputs)
 print(tokenizer.decode(outputs[0]))
 ```
 
-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).
 
 ## Evaluation
 
@@ -190,10 +190,13 @@ Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
 - **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 post-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 released in the following weeks
 - 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/651e96991b97c9f33d26bde6/1umKwihvgLPlj5_0xJ42j.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.