Model Detail
Llama-3.2-1B-Instruct
▼ 0.8%Llama-3.2-1B-Instruct is a large language model with 1B 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 llama3.2 license.
Open-LLM-Leaderboard scoring places it at MMLU-Pro 8, GPQA 2, IFEval 58, BBH 8, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.
Llama-3.2-1B-Instruct is priced at $0.027/M input tokens and $0.2/M output tokens. Operationally the model offers a 131K-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.
Llama-3.2-1B-Instruct ships as a LlamaForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 1B 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 1.2 GB, which is the relevant figure when planning local-inference VRAM. Access is gated on Hugging Face under the llama3.2 license, which means a manual approval step before weights can be downloaded.
Downloads of Llama-3.2-1B-Instruct have moved -0.8% over the past 24 hours, -1.4% over the trailing seven days, +29.6% over the trailing thirty days. 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.
Llama-3.2-1B-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.
The Capacity of Thought: Benchmarking Llama 3.2 in Semantic fMRI Neural Language Decoding and Improving the Huth Encoding-Model Baseline
arXiv:2607.12079v1 Announce Type: new Abstract: Decoding continuous language from fMRI signals remains a core challenge in non-invasive brain-computer interface research. We present two complementary investigations. First, we improve the Huth et al. ridge regression encoding pipeline through expande
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Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning
arXiv:2606.15507v1 Announce Type: new Abstract: Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it. We use Transluce, an AI-driven mechanistic-interpretability platform, to examine LLaMA 3.1-8B-Instruct on 54 moral promp
Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2
arXiv:2512.22671v3 Announce Type: replace-cross Abstract: Structured width pruning of GLU-MLP layers in Llama-3.2 models, guided by the Peak-to-Peak Magnitude (PPM) criterion, reveals a systematic dichotomy in how reducing the expansion ratio affects different model capabilities. While performance o
A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget
arXiv:2606.13370v1 Announce Type: new Abstract: This study examines training dynamics in a small Llama-style language model trained under a fixed, compute-constrained token budget. Rather than evaluating efficiency solely through endpoint performance, the study uses a quantitative experimental repea
The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring
arXiv:2606.10327v1 Announce Type: new Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.g., lead, claim, evidence, conclusion), yet most approaches treat these in isolation, harming coherence and generalization. We investigate task-aware fine-tuning of L
