Llama-3.1 8B: Open-weight Transformer Model

• Llama-3.1 8B is an open-weight Transformer language model with 8 billion parameters, designed to handle extensive contexts and diverse domain applications.
• It employs a 32-layer causal-decoder architecture with support for up to 128,000 tokens per context window, enabling complex document processing.
• The model forms the foundation for various specialized adaptations through continued pretraining and instruction tuning, yielding improved performance in fields like astronomy, cybersecurity, and multilingual NLP.

Llama-3.1 8B is an open-weight, Transformer-based LLM with approximately 8 billion parameters developed by Meta, serving as the foundational architecture for a variety of domain-specialized and language-adapted LLMs. This model, released under an open-weight license, was pretrained on over 15 trillion tokens of publicly accessible text and supports up to 128,000 tokens per context window, facilitating the processing of long-form documents and complex information extraction tasks across diverse fields (Garcia-Alcoser et al., 3 Jun 2025, Haan et al., 13 Nov 2024, Kassianik et al., 28 Apr 2025, Roussis et al., 19 May 2025, Sakib et al., 12 Mar 2025).

1. Architecture and Pretraining Characteristics

Llama-3.1 8B employs a causal-decoder Transformer architecture structured as 32 decoder blocks, each consisting of multi-head self-attention (32 heads, multi-query scaled dot-product attention), two-layer MLP with GELU activations, and layer normalization. The hidden dimension is 4,096 with a feed-forward inner dimension of 11,008. Relative position bias is incorporated to enhance long-range dependency modeling, and the design accommodates a context window up to 128k tokens (although many downstream applications configure for efficiency at 4k or 8k). The base vocabulary and tokenizer are derived from the original Llama-3.1 design, with some domain adaptations employing token expansion (Kassianik et al., 28 Apr 2025, Roussis et al., 19 May 2025).

Pretraining was conducted on a collection exceeding 15 trillion tokens, using instruction-based supervised learning. No radiology-specific or domain-specific fine-tuning was included in the reference clinical evaluation; all results were obtained in a strict zero-shot context (Garcia-Alcoser et al., 3 Jun 2025).

2. Specialization, Adaptation, and Variants

Llama-3.1 8B serves as the basis for multiple specialized adaptations through continued pretraining (CPT), supervised fine-tuning (SFT), and instruction alignment:

• AstroSage-Llama-3.1-8B (Haan et al., 13 Nov 2024): Domain-specialized for astronomy, trained on astronomical literature and synthetic Q&A, achieving 80.9% accuracy on AstroMLab-1, outperforming other 8B-class models and matching GPT-4o.
• Foundation-Sec-8B (Kassianik et al., 28 Apr 2025): Continued pretraining on a 5.1B-token cybersecurity corpus yields significant improvements on cybersecurity MCQA (+6.3% over base Llama-3.1 8B), closing much of the gap to 70B and GPT-4o-mini models without architectural modifications.
• Llama-Krikri-8B (Roussis et al., 19 May 2025): Greek/English/Ancient Greek model with expanded vocabulary (+20,992 tokens), resulting in large gains in Greek fluency and task accuracy (Greek accuracy: 59.5% vs. Llama-3.1-8B baseline: 48.7%).
• Instruction Tuning and Fine-Tuning Transfer (Lin et al., 25 Mar 2025): Introduction of “diff vector” transfer enables rapid adaptation and upgrade of Llama-3.1 8B using fine-tuned weight changes from previous versions, with empirical results demonstrating substantial downstream accuracy gains (GPQA: +10.7% absolute, surpassing direct instruct tuning on the same version).

These variants demonstrate the flexibility of Llama-3.1 8B in absorbing specialized knowledge while maintaining general capabilities.

3. Evaluation Metrics and Performance Profiles

Multiple evaluation protocols are used to characterize Llama-3.1 8B and its derivatives:

• Cohen’s Kappa (κ): Quantifies agreement with human or model references beyond chance:

$\kappa = \frac{p_o - p_e}{1 - p_e}$

where $p_o$ is observed agreement and $p_e$ is the expected agreement.

• F1 Score (micro/macro): Balances precision and recall for classification.

$$F1 = 2 \times \frac{\mathrm{Precision} \times \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$

• Attack Success Rate (ASR) and Detection Rate (DR): Used in adversarial testing scenarios (Sakib et al., 12 Mar 2025).

Key Benchmark Performances:

Domain	Task/Benchmark	Llama-3.1 8B Score	Peer/Open Model Performance
Radiology Reports	Macro-F1 (CT)	0.79 (0.77–0.81)	Gemma-3 27B: 0.82 (0.80–0.83)
Astronomy	AstroMLab-1	72.9%	AstroSage-Llama-3.1-8B: 80.9% (GPT-4o: 80.4%)
Cybersecurity	CTIBench-MCQA	0.623 ± 0.012	Foundation-Sec-8B: 0.662 ± 0.007
Greek NLP	Greek Accuracy	48.7%	Krikri-8B: 59.5%
Adversarial Factuality	ASR (strong confidence)	4.78%	Falcon (7B): 73.68%

On clinical report annotation, Llama-3.1 8B achieves “almost perfect” agreement with Gemma-3 27B ($\kappa$ median 0.87), and on CT-RATE (external validation) yields highest macro-F1 (0.91) for lungs/pleura (Garcia-Alcoser et al., 3 Jun 2025). Specialized continued pretraining or vocab expansion consistently yields substantial domain-localized gains (Haan et al., 13 Nov 2024, Kassianik et al., 28 Apr 2025, Roussis et al., 19 May 2025).

4. Zero-Shot, Prompting, and Generalization Capabilities

Llama-3.1 8B demonstrates strong capacity for zero-shot prompting across domains:

• Structured zero-shot: For radiology, the model was prompted to return JSON dictionaries with binary label assignments for 15 disease classes, without in-domain examples (Garcia-Alcoser et al., 3 Jun 2025).
• Cross-system generalization: No organ system–specific adaptation was necessary; near-equivalent kappa and F1 were measured across Kidney/Ureter, Liver/Gallbladder, and Lung/Pleura tasks, without fine-tuning.
• Sensitivity to label definitions: Performance (e.g., on “atelectasis”) shifts with annotation stringency, confirming that Llama-3.1 8B models actual report language, not merely static criteria.

Specialized adaptations (e.g., continued pretraining on astronomy or cybersecurity literature, vocabulary expansion for Greek) yield rapid and substantial improvements on domain-relevant benchmarks—even when the base Llama-3.1 8B already demonstrates strong general task transfer (Haan et al., 13 Nov 2024, Kassianik et al., 28 Apr 2025, Roussis et al., 19 May 2025).

5. Fine-Tuning Transfer and Efficient Model Evolution

An emerging methodology, “additive fine-tuning transfer,” utilizes diff vectors ($\delta$) to transfer instruction/fine-tuning improvements from one model version to another. Let $$\delta_s = \theta^{(s)}_{\mathrm{fine}} - \theta^{(s)}_{\mathrm{base}}$$ be the source model’s fine-tuning delta.

Transferred directly onto a new base ($\theta_t^{\mathrm{base}}$), this yields improved accuracy (e.g., on GPQA, base Llama-3.1 8B: 21.9%; with $\delta_{3.0}$: 32.6% (Lin et al., 25 Mar 2025)). Empirical results reveal this is effective when both source and target occupy a “linearly connected” parameter-space region. Iterative recycling (sequential application and fine-tuning of transferred deltas) further improves convergence speed and training efficiency.

6. Robustness and Adversarial Behavior

In adversarial factuality testing, Llama-3.1 8B displays unusually high robustness against strongly confident misinformation (ASR 4.78%, DR 95.22%), compared to other open-weight LLMs (e.g., Falcon 7B ASR 73.68%) (Sakib et al., 12 Mar 2025). The model is more susceptible to subtler, low-confidence adversarial prompts (ASR up to 10.05%), a reversal of the trend observed in most peer models, indicating reliance on assertive cues to trigger internal fact-checking. Adversarial attacks most effective against Llama-3.1 8B tend to focus on ambiguities or partially true premises.

Summary of Adversarial Robustness (ASR by Confidence Level):

Confidence Tier	Llama-3.1 8B ASR	Trend Compared to Peers
Strongly Confident	4.78%	Exceptionally robust
Moderately Confident	7.66%	Performance drops
Limited Confident	10.05%	Most vulnerable

This behavior suggests Llama-3.1 8B's training and alignment optimizations favor factual correction in the presence of strong assertions but are comparatively less vigilant against low-confidence, nuanced manipulations.

7. Limitations and Integration in Practice

Although Llama-3.1 8B demonstrates strong generalization and zero-shot performance, several limitations are documented:

• Nuance loss in binary labels: Binary classification misses subtleties in medical reasoning (e.g., “too small to characterize lesion”) and cannot fully capture radiological report language (Garcia-Alcoser et al., 3 Jun 2025).
• Subjectivity and variability: Inter-annotator disagreement and local labeling conventions may lead to systematic differences between manual and model outputs.
• Domain-anchored limitations: Domain-specialized continued pretraining (e.g., cybersecurity, astronomy, Greek language) may lead to modest declines in unrelated general task performance, though catastrophic forgetting is not observed (Kassianik et al., 28 Apr 2025).
• Production recommendations: Hybrid systems (LLM + rule-based), precise label definitions, and human-in-the-loop review are advised for high-stakes deployment.

Llama-3.1 8B, by virtue of its open-weight license, extensive context support, and efficiency in domain transformation, provides a foundation for domain-specific LLM pipelines, scalable task adaptation via prompt engineering, and robust integration into research and clinical informatics systems (Garcia-Alcoser et al., 3 Jun 2025, Lin et al., 25 Mar 2025, Haan et al., 13 Nov 2024, Kassianik et al., 28 Apr 2025, Roussis et al., 19 May 2025, Sakib et al., 12 Mar 2025).