Model Detail
Meta-Llama-3-8B-Instruct
—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.
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.
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.
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.
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.
Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation
arXiv:2607.18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata. We show that when domain vocabulary and semantics are captured in a well-designed Web
On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
arXiv:2607.16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems. Their ability to generalize across topologies opens the prospect of a surrogate for combined structural and parametric optimi
A multiverse-consensus pipeline for reproducible feature selection in untargeted LC-MS metabolomics
arXiv:2607.17345v1 Announce Type: new Abstract: Background: Untargeted LC-MS metabolomics requires a long chain of preprocessing decisions, each with several equally defensible options. Analysts typically commit to one pipeline and report the resulting feature shortlist. How strongly that shortlist
Harnessing disorder to decouple extension and shear in kirigami metamaterials
arXiv:2607.16583v1 Announce Type: cross Abstract: Kirigami turns stiff sheets into compliant, shape-morphing structures, but its reliance on periodic cut patterns comes at a cost: correlated panel rotations couple extension to shear, so stretching one axis drives a parasitic shear that cannot be sup
Metadata-Free Meta-Reweighted Direct Preference Optimization under Noisy Preference Labels
arXiv:2607.09796v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) has become an important method for aligning large language models (LLMs) with human preferences because it removes the need for explicit reward modeling and reinforcement learning. However, its performance depen
MTSSL: Meta-Thresholding Semi-Supervised Learning
arXiv:2607.16363v1 Announce Type: cross Abstract: A large body of Semi-supervised Learning~(SSL) algorithms encounter the threshold $\tau$ to select pseudo-labels. The value of $\tau$ across different SSL algorithms can vary depending on the learning perspective, yet they may achieve similar perform