Update README.md

README.md

@@ -208,7 +208,7 @@ You can directly run and test the model with this [Colab notebook](https://colab
 You need to install [`vLLM`](https://github.com/vllm-project/vllm) v0.10.2 or a more recent version as follows:
 
 ```bash
-uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto
+uv pip install vllm==0.10.2 --extra-index-url https://wheels.vllm.ai/0.10.2/ --torch-backend=auto
 ```
 
 Here is an example of how to use it for inference:
@@ -223,7 +223,7 @@ prompts = [
 ]
 sampling_params = SamplingParams(temperature=0.3, min_p=0.15, repetition_penalty=1.05)
 
-llm = LLM(model="LiquidAI/LFM2-350M")
+llm = LLM(model="LiquidAI/LFM2-2.6B")
 
 outputs = llm.generate(prompts, sampling_params)
 
@@ -257,10 +257,10 @@ LFM2 outperforms similar-sized models across different evaluation categories. We
 | Model                  | MMLU  | GPQA  | IFEval | IFBench | GSM8K | MGSM  | MMMLU |
 | ---------------------- | ----- | ----- | ------ | ------- | ----- | ----- | ----- |
 | LFM2-2.6B              | 64.42 | 26.57 | 79.56  | 22.19   | 82.41 | 74.32 | 55.39 |
-| Qwen3-4B-Instruct-2507 | 72.25 | 34.85 | 85.62  | 30.28   | 68.46 | 81.76 | 60.67 |
+| Llama-3.2-3B-Instruct  | 60.35 | 30.6  | 71.43  | 20.78   | 75.21 | 61.68 | 47.92 |
 | SmolLM3-3B             | 59.84 | 26.31 | 72.44  | 17.93   | 81.12 | 68.72 | 50.02 |
 | gemma-3-4b-it          | 58.35 | 29.51 | 76.85  | 23.53   | 89.92 | 87.28 | 50.14 |
-| Llama-3.2-3B-Instruct  | 60.35 | 30.6  | 71.43  | 20.78   | 75.21 | 61.68 | 47.92 |
+| Qwen3-4B-Instruct-2507 | 72.25 | 34.85 | 85.62  | 30.28   | 68.46 | 81.76 | 60.67 |
 
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