LLaMA 3.1 8B Model Overview

Updated 9 November 2025

LLaMA 3.1 8B is an open, dense Transformer model with 8B parameters, designed for multilingual natural language and multimodal applications.
Pretrained on 15 trillion tokens with rigorous filtering and advanced alignment, it demonstrates strong zero- and few-shot performance on benchmarks.
Efficient transfer learning and domain-specific fine-tuning make it ideal for specialized research in cybersecurity, astronomy, medicine, and more.

The LLaMA 3.1 8B model is an open, dense, decoder-only Transformer-based LLM featuring approximately 8 billion parameters. As part of the LLaMA 3 "herd" of models, it is designed for general-purpose natural language understanding and generation, with strong support for multilinguality, reasoning, coding, tool usage, and extensibility to multimodal domains via adapters. With open weights and permissive licensing, the LLaMA 3.1 8B serves as a robust foundation for research and real-world applications in both academic and specialized domains, and is a frequent base for instruction-tuned, domain-adapted, and efficiency-optimized variants.

1. Architecture and Model Specification

LLaMA 3.1 8B is implemented as a dense Transformer without mixture-of-experts (MoE), configured as follows:

Parameter	Value	Description
Number of layers	32	Transformer decoder blocks
Hidden size	4096	Model (per-token embedding) dimension
FFN inner dim	14,336	Feedforward submodule hidden size
Attention heads	32 (8 KV heads)	Head count (grouped-query configuration)
Activation	SwiGLU
Positional encoding	RoPE ( $\theta$ =500,000)	Rotary position embeddings
Vocabulary size	128,000	BPE (tiktoken-based, extended non-English)
Context window	8,192 (up to 128k)	Pretraining, extensible post-training
Total parameters	≈8,000,000,000	As reported

The RoPE positional encodings use a high base allowing extensibility up to 128k-token context windows after continued pretraining. Inference is feasible on a single H100-80GB GPU (no model-parallelism required). The architecture natively supports compositional adapters for image, video, and speech modalities (adapter weights for 8B released March 2025; see (Grattafiori et al., 31 Jul 2024)).

2. Pretraining Data, Objectives, and Alignment

The pretraining corpus for LLaMA 3.1 8B comprises approximately 15 trillion tokens, subject to rigorous deduplication, filtration, and quality control:

50% general web text
25% mathematical/reasoning sources
17% code (multi-language)
8% multilingual content (active non-English vocabulary expansion)

Data was filtered for PII, adult content, and spurious duplicates (n-gram/Bloom filter deduplication), with HTML and markup cleaning. Training used AdamW with cosine learning rate decay and linear warmup (peak LR $3\times 10^{-4}$ for 8B), starting with 4M tokens/step (length 4096), later scaling to 16M tokens/step. Compute footprint totaled approximately $3.8 \times 10^{25}$ FLOPs.

Post-training alignment involved a multi-phase recipe:

Supervised fine-tuning (SFT) on mixed helpfulness/safety corpora (~400K examples).
Human preference modeling (reward model scored).
Direct preference optimization (DPO) on recent preference batches.
Model averaging and iterative complexity ramp-up over 6 training rounds.

Safety finetuning incorporated explicit adversarial, borderline, and helpful data; instruction-tuning sampled from both human-authored and synthetic sources spanning code, tool-usage, reasoning, and multi-turn dialogue.

3. Empirical Performance Across Benchmarks

LLaMA 3.1 8B displays competitive or state-of-the-art results among open models of similar size for diverse tasks (benchmark settings: mostly zero- or few-shot):

Category	Benchmark	LLaMA 3.1 8B Accuracy (%)
General knowledge	MMLU (5-shot)	69.4
	MMLU (0-shot, CoT)	73.0
Code generation	HumanEval (0-shot)	72.6 (pass@1)
Math reasoning	GSM8K (8-shot, CoT)	84.5
	MATH (0-shot, CoT)	51.9
Reasoning	ARC (0-shot)	83.4
	GPQA (0-shot, CoT)	32.8
Tool use	BFCL	76.1
	Nexus	38.5
Long context	NIH/Multi-needle	98.8
Multilingual	MGSM (0-shot, CoT)	68.9

LLaMA 3.1 8B achieves >70% pass@1 on code, excels in long-context document retrieval and complex reasoning, and leads its parameter class on a range of public benchmarks (e.g., outperforming Mistral 7B and Gemma 2 9B in most domains). Inference throughput on H100 is ~120 tokens/s/GPU at batch=1.

4. Specialization, Domain Adaptation, and Fine-tuning

The architecture and open availability of LLaMA 3.1 8B facilitate continued pretraining and efficient fine-tuning for domain-specific or task-specific variants:

SecurityLLM (Foundation-Sec-8B, (Kassianik et al., 28 Apr 2025)): Continued-pretraining on a filtered 5.1B-token cybersecurity corpus (CTI, CVE/CWE, etc.) yields +14.3% improvement on root-cause mapping and >10% TTP extraction gains relative to base, while sacrificing only 1–2 points on general MMLU.
AstroMLab 3 (AstroSage-LLaMA-3.1-8B, (Haan et al., 13 Nov 2024)): Domain specialization using 3.3B astronomy tokens and synthetic Q&A raises astronomy MCQA to 80.9% (base model: 72.9%), matching GPT-4o and Llama-3.1-70B-Instruct on AstroMLab-1.
Sherkala-Chat 8B (Koto et al., 3 Mar 2025): Kazakh-centric, multilingual continued-pretraining (45.3B tokens) and instruction tuning achieves 47.6% on Kazakh MCQA (vs. 39.8% for base), 59.1% on English, with strong safety alignment (91.9% safe responses) and broad language support.
Medical domain adaptation (Wei et al., 25 Sep 2024): Fine-tuning via LoRA on weak/synthetic labels in radiology achieves micro-F1=0.91 on open-ended disease detection (approaching the GPT-4o synthetic teacher), and 0.67 F1 on noisy-labeled multiple-choice, outperforming the rule-based labeling baseline.

In all cases, adaptation involves no changes to the core architecture; specialization arises from data selection, task mixing, and (for instruction-tuning) additional alignment and safety procedures. Context extension and BPE vocabulary adaptation are supported.

5. Transfer Learning and Development Efficiency

Efficient development is achievable via diff-vector fine-tuning transfer, enabling rapid recycling of task-specific updates across model releases (Lin et al., 25 Mar 2025):

Let $m_s$ be a source model (e.g., Llama 3.0 8B), $m^\prime_s$ its fine-tuned state, and $m_t$ a target base (Llama 3.1 8B). The diff $\Delta_s = m^\prime_s - m_s$ is added to $m_t$ , i.e., $m^\prime_t \approx m_t + \Delta_s$ , under the hypothesis of local linearity in parameter space.

Recycling the instruction diff from 3.0 to 3.1 8B recovers ~60–80% of instruct-tuning gains at zero additional training (GPQA: +10.7 points, IFEval: +46.9 points), with only minor MMLU impact (–1.7 points).
Multilingual diff transfer (Malagasy/Turkish) yields up to +15.5% over Llama 3.1 Instruct on Global MMLU.
Iterative recycling-then-fine-tuning further accelerates convergence and can outperform standard fine-tuning by up to 6.6 points in controlled experiments, halving compute requirements.

Transfer is most effective when models are "linearly connected" in parameter space—i.e., the base and fine-tuned endpoints are not too distant in training progression or architecture, and are empirically aligned via low-loss linear paths.

6. Inference Characteristics, Scaling Laws, and Multimodal Extensions

Inference for LLaMA 3.1 8B is highly efficient on current hardware. Model fits entirely within an 80GB H100. FP8 inference yields an estimated 1.3–1.5× speed-up with negligible quality loss (for larger models, explicitly tested).

The compute-optimal token budget $N^*(C) = A C^\alpha$ (with $A \approx 0.29$ , $\alpha \approx 0.53$ ) is observed, and isoFLOPs curves enable forecasting of negative log-likelihood and downstream accuracy prior to full scaling runs. Downstream accuracy is mapped from pretrain loss via a sigmoid, providing an empirical scaling law for planning.

Long-context capabilities reach up to 128k tokens following continued pretraining. Tool augmentation (function calling, code interpreter, Wolfram|Alpha, search) is managed through prompt-level integration and dedicated instruction-tuning. Safety is improved with LLaMA Guard 3, an 8B-parameter classifier filtering input/output across 13 harm categories and code abuse.

Multimodal potential is realized by compositional adapters, enabling the core model to interface image, video, or speech modules (adapter weights released March 2025 for the 8B backbone).

7. Availability, Licensing, and Applications

LLaMA 3.1 8B is released under the LLaMA 3 Community License (April 2024), with both pre-trained and instruction-tuned checkpoints openly available. The model serves as a widely used foundation for:

General and multilingual NLP
Domain-specialized assistants (e.g., cybersecurity, astronomy, medicine, low-resource languages)
Tool-augmented applications (code, reasoning, search)
Research in alignment, transfer learning, and efficient adaptation
Multimodal research and competitive low-cost inference at scale

Open release, robust alignment and safety features, and demonstrated adaptability make LLaMA 3.1 8B a central model for academic and applied development across diverse AI workflows.