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Model Detail

meta-llama logo

Meta-Llama-3-8B-Instruct

▼ 3.5%
Provider: MetaCategory: llmPipeline: text-generationParameters: 8B
DB Score
15.2
Downloads
1.4M
Likes
5K
Day
-3.5%
Week
+0.0%
Month
+0.0%
Overview

Meta-Llama-3-8B-Instruct is a large language model with 8B parameters released by Meta. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, released under the llama3 license.

Performance

Open-LLM-Leaderboard scoring places it at MMLU-Pro 29, GPQA 6, IFEval 48, BBH 27, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.

How we score this →
Pricing & Throughput

Meta-Llama-3-8B-Instruct is priced at $0.15/M input tokens and $0.15/M output tokens. Operationally the model offers a 8K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Technical

Meta-Llama-3-8B-Instruct ships as a LlamaForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 8B parameters. The published knowledge cutoff is 2023-12-31, so newer events will not be reflected in zero-shot answers without retrieval. Total weight footprint is approximately 8.0 GB, which is the relevant figure when planning local-inference VRAM. Access is gated on Hugging Face under the llama3 license, which means a manual approval step before weights can be downloaded.

Trending Signal

Downloads of Meta-Llama-3-8B-Instruct have moved -3.5% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

Meta-Llama-3-8B-Instruct is best fit for general-purpose chat and instruction-following workloads, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$0.15
Output ($/M tokens)
$0.15
Context Window
8K
Benchmark Scores
IFEval
47.8
BBH
26.8
GPQA
5.7
MMLU-Pro
28.8
MATH
9.1
MUSR
5.4
Average
20.6
Model Info
Licensellama3
ArchitectureLlamaForCausalLM
Type💬 chat models (RLHF, DPO, IFT, ...)
Modalitytext->text
Knowledge Cutoff2023-12-31
Citations18,118 (3355 influential)
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