update docs

docs.md

@@ -22,11 +22,7 @@ Once you've trained a model, you'll need to push it to a repo on the Hugging Fac
 
 ```bash
 hf jobs uv run \
-    --flavor a100-large \
-    --secrets HF_TOKEN \
-    "trl/scripts/sft.py" \
-    ...
-    --push_to_hub # this will push the model to the Hugging Face Hub
+    # this will push the model to the Hugging Face Hub
 ```
 
 Your trained model will be available at `your-username/your-model-name`. For detailed documentation, check out the [checkpoints documentation](https://huggingface.co/docs/transformers/trainer#checkpoints) from `transformers`.
@@ -39,7 +35,7 @@ Now, we will need to evaluate the model. We will use `hf jobs` to evaluate the m
 hf jobs uv run \ # run a hf jobs job with uv
 --flavor a10g-large \ # select the machine size
 --with "lighteval[vllm]" \ # install lighteval with vllm dependencies
-s HF_TOKEN \ # share the huggingface write token
+-s HF_TOKEN \ # share the huggingface write token
 lighteval vllm "model_name=<your-username>/<your-model-name>" "lighteval|gsm8k|0|0" --push-to-hub --results-org <your-username>
 ```
 
@@ -72,7 +68,7 @@ Open a pull request on the [leaderboard space](https://huggingface.co/spaces/smo
             "submission_date": "2025-09-02",
             "results-dataset": "smol-course/details_HuggingFaceTB__SmolLM3-3B_private"
         },
-        
+
         ... # existing submissions
 
         {