Side-by-side model comparison

Llama-3.1-8B-Instruct vs SmolLM-1.7B-Instruct-quantized.w4a16

Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.

Model A

meta-llama/Llama-3.1-8B-Instruct

Benchmark score 98.50

Parameters 8.00B

Model family Llama

Dataset status Available

Model B

nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16

Benchmark score 98.50

Parameters 1.70B

Model family Llama

Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset

Metric	Llama-3.1-8B-Instruct	SmolLM-1.7B-Instruct-quantized.w4a16	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	8.00B	1.70B	+6.3B (+370.6%)
Model family	Llama	Llama	Match

Performance Verdict

Based on the available leaderboard data, meta-llama/Llama-3.1-8B-Instruct has the stronger overall benchmark score.

meta-llama/Llama-3.1-8B-Instruct is the stronger performer, scoring 98.50 on average compared to nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16's 98.50.
meta-llama/Llama-3.1-8B-Instruct is 370.6% larger in parameter capacity than nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 (8.00B vs 1.70B parameters).
meta-llama/Llama-3.1-8B-Instruct has more parameter capacity, which may contribute to its stronger benchmark score.

Integration & Implementation Guide

Learn how to load and execute these models programmatically in Python using Hugging Face's transformers library.

Python tutorial

Load Model A (Llama-3.1-8B-Instruct)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

Load Model B (SmolLM-1.7B-Instruct-quantized.w4a16)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16")

Compare Alternative Models

Explore nearby pairings from the same model dataset.

Comparison

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meta-llama/Llama-3.1-8B-Instruct

nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16

Metric Comparison

Performance Verdict

Integration & Implementation Guide

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