Llama 3.1: Scalable Multilingual & Code Model

• Llama 3.1 is a family of transformer-based, decoder-only language models that offer scalable architectures with expanded multilingual and multimodal capabilities.
• The models employ efficient fine-tuning methods like QLoRA by freezing quantized base weights and optimizing low-rank matrices, reducing computational overhead.
• Llama 3.1 demonstrates strong performance in radiology reporting, code generation, and low-resource NLP, achieving state-of-the-art results on several benchmarks.

Llama 3.1 refers to the family of large-scale transformer-based decoder-only LLMs developed by Meta and its derivatives, which serve as the foundation for a range of high-performing NLP and multimodal systems. Llama 3.1 distinguishes itself from earlier Llama releases through increased model scale, expanded multilingual capabilities, and support for efficient fine-tuning techniques, facilitating broad applications in code generation, healthcare, and instruction-following in low-resource languages.

1. Architectural Variants and Properties

Llama 3.1 encompasses a spectrum of model sizes and customizations. The canonical “8B” variant is a 32-layer transformer comprising approximately $8\times 10^9$ parameters, with a model dimensionality of 4 096 and 32 self-attention heads per block. The 405B variant, at 405 billion parameters, occupies the upper-envelope of open LLMs by scale, but specific hyperparameters remain undisclosed (Deroy et al., 26 Sep 2024).

All Llama 3.1 models utilize decoder-only causal attention and dense feedforward networks, with rotary position embeddings (RoPE) for extended context and grouped-query attention (GQA) optionally available, further enhancing inference throughput (Koto et al., 3 Mar 2025). Vocabulary is extended beyond the baseline Llama token set to accommodate low-resource and multilingual settings, often via weighted initialization from the base embedding matrix.

Variant	Parameters	Layers	Hidden Dim	Heads / Layer	Notable Features
8B	8B	32	4 096	32	Open, widely fine-tuned
405B	405B	–	–	–	Proprietary, code-focused
Sherkala-8B	8B	32	4 096	32	Multilingual, Kazakh-tuned

Exact values for the 405B variant remain unspecified.

2. Fine-Tuning and Adaptation Methodologies

Memory-efficient adaptation of Llama 3.1 is enabled via Quantized Low-Rank Adaptation (QLoRA), as exemplified by its integration in the LLaMA-XR framework for radiology report generation (Jahangir et al., 29 May 2025). In this regime, the quantized base model weights $W_0$ are frozen and paired with trainable low-rank matrices $A$ and $B$ in each attention and feedforward projection. The adapted weight at inference is computed as $W = W_0 + AB$, allowing for full-precision updates to a compact parameter subset (roughly 41.9M trainable parameters for the LoRA modules in the 8B model), while the majority of the network resides in 4-bit quantized memory, yielding significant computational savings. Optimization is performed using an 8-bit variant of AdamW (“adamw_8bit”).

Sherkala-8B-Chat employs instruction-tuned supervised fine-tuning (SFT) at scale, with multilingual prompt mixes and safety alignment driven by human-validated adversarial examples and refusal prompts (∼200K Kazakh and ∼100K English for safety tuning). No specialized loss beyond cross-entropy is introduced, and language quality is further refined via replay mixes balancing source and target language content (Koto et al., 3 Mar 2025).

3. Multilingual and Domain-Specific Extensions

Llama 3.1’s architectural modularity enables adaptation to new languages and modalities. The Sherkala-8B-Chat (8B) model is derived via continual pretraining on 45.3B tokens spanning Kazakh (19.45B), English (19.45B), Russian, and Turkish, utilizing a 3:1:3 mixture to avoid catastrophic forgetting in English while maximizing Kazakh domain knowledge. The tokenizer is extended by 25% to include new subwords relevant for Kazakh and regional languages, initialized using weighted averages over nearest base embeddings.

Visual–language integration in LLaMA-XR combines DenseNet-121 visual features with the Llama 3.1 LLM backbone. Frontal and lateral chest X-rays are encoded as two separate 18-dimensional vectors, concatenated to form a 36-dimensional global image embedding. This is serialized into a natural-language prompt and tokenized, then prepended to the generation context. The fused token sequence is processed as standard instruction tokens, requiring no architectural cross-attention or vision modules (Jahangir et al., 29 May 2025).

4. Code Generation and Capabilities

The Llama 3.1 405B model demonstrates notable competence in code generation by translating natural-language prompts into syntactically correct and expert-verified code across diverse programming languages and algorithmic paradigms (Deroy et al., 26 Sep 2024). Zero-shot and few-shot performance are highlighted, with the model achieving:

• 94% correctness in Algorithms
• 98% in Programming & Data Structures (PDS)
• 67% in AI
• 56% in Bioinformatics (BioA)
• 54% in Quantum Computing (QC)

Expert human ratings for code relevance (4.84/5) and completeness (4.43/5) further corroborate its effectiveness. However, accuracy decreases in highly specialized domains due to limited training data or absence of domain-specific reasoning capacity.

Contextual awareness enables Llama 3.1 to perform iterative debugging: refining code upon user request, identifying logical or syntactic errors, suggesting modular refactorings, and providing inline commentary on algorithmic complexity, all within a natural conversational interface.

5. Evaluation Benchmarks and Metrics

Llama 3.1–derived models are assessed across a spectrum of established benchmarks:

LLaMA-XR: Evaluated for radiology report generation on the IU-Xray corpus using ROUGE-L and METEOR scores, achieving 0.433 and 0.336, respectively, surpassing previous SOTA by 4.34% (ROUGE-L) and 54.13% (METEOR) (Jahangir et al., 29 May 2025).

Sherkala-8B-Chat: Benchmarked on KazMMLU, MMLU, and several reading comprehension and commonsense reasoning tasks. Achieves 47.6% accuracy on KazMMLU (state-of-the-art for Kazakh open LLMs), 32.0% on Russian, and remains competitive on English-centric tasks. Text generation quality is further validated by GPT-4o judgments, with generated content outperforming baseline models in Kazakh and performing competitively in English (Koto et al., 3 Mar 2025).

Model Variant	Kazakh MMLU	Russian Eval	English MMLU	ROUGE-L (Radiology)
Sherkala-Chat 8B	47.6%	32.0%	59.1%	–
LLaMA-XR 8B	–	–	–	0.433

Safety evaluation employs do-not-answer datasets across multiple harm types, with Sherkala-Chat achieving safe response rates of 91.9% (Kazakh) and 85.1% (Russian).

6. Practical Applications and Limitations

Llama 3.1 powers diverse production and research systems:

• Radiology Reporting: Automated, prompt-based multimodal report generation with superior semantic and clinical consistency under constrained hardware budgets (Jahangir et al., 29 May 2025).
• Algorithmic Code Synthesis: Zero-shot, multi-language, and context-aware code generation verified by human experts; rapid prototyping and educational utility demonstrated (Deroy et al., 26 Sep 2024).
• Multilingual and Low-Resource NLP: State-of-the-art open LLM for Kazakh, inclusive of comprehensive safety alignment and competitive performance in Russian and English (Koto et al., 3 Mar 2025).

Notable limitations include diminished reliability in domains requiring deep, formal expertise (Quantum Computing, advanced ML, Bioinformatics), sensitivity to domain transfer, and dependence on high-quality in-domain or target-language data for effective fine-tuning.

7. Future Research Directions

Potential avenues for advancing Llama 3.1 and its applications include:

• Scaling to larger parameter counts (e.g., 34B, 70B) for improved knowledge representation, as suggested by the Sherkala-Chat authors.
• Diversification of language resources and domain-specific corpora (e.g., legal, medical) for extensible multilingual support.
• Exploration of reinforcement learning from human feedback (RLHF) for enhanced safety and alignment.
• Development of hybrid neuro-symbolic systems to address persistent code generation challenges in scientific domains.
• Adaptation recipes for extending Llama 3.1 to other low-resource languages, leveraging the tokenizer and pretraining protocols demonstrated in Sherkala.

These efforts collectively mark Llama 3.1 as a flexible and efficient platform for multilingual, multimodal, and domain-adapted language modeling, with empirical support across various high-impact application domains (Koto et al., 3 Mar 2025, Jahangir et al., 29 May 2025, Deroy et al., 26 Sep 2024).