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Finding the Best SmolLM for Your Project 
Finding the Best SmolLM for Your Project 
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Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? 🤔
Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? 🤔
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If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: 
If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: 
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https://huggingface.co/spaces/open-llm-leaderboard/comparator
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open-llm-leaderboard/comparator
https://huggingface.co/spaces/open-llm-leaderboard/comparator
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Let’s walk through an example👇
Let’s walk through an example👇
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Let’s compare two solid options:
Let’s compare two solid options:
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- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
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- gemma-2-2b-it from Google (2.5B params)
- gemma-2-2b-it from Google (2.5B params)
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For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?
For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?
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What about other evaluations?
What about other evaluations?
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Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! 📊
Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! 📊
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This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.
This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.
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Looking for other comparisons? Drop your model suggestions below! 👇
Looking for other comparisons? Drop your model suggestions below! 👇
Finding the Best SmolLM for Your Project 

Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? 🤔

If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: https://huggingface.co/spaces/open-llm-leaderboard/comparator
Let’s walk through an example👇

Let’s compare two solid options:
- Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params)
- gemma-2-2b-it from Google (2.5B params)

For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?

What about other evaluations?
Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! 📊

This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.

Looking for other comparisons? Drop your model suggestions below! 👇
👀
John6666 victor pawelg27 Sebuzdugan krishaamer AtAndDev
🔥
Sebuzdugan AtAndDev