LLaMA-33B: Scalable Multilingual Transformer

• LLaMA-33B is a transformer-based language model with 33 billion parameters, featuring an extended, merged tokenizer that enhances Chinese text processing.
• Its secondary pre-training on large-scale Chinese corpora and LoRA parameter-efficient fine-tuning enable effective adaptation to code, math, and diverse NLP tasks.
• Innovative hardware deployment strategies, including quantization and ROMA accelerator design, facilitate high throughput and on-device inference efficiency.

LLaMA-33B is a large-scale, transformer-based LLM originating from the LLaMA (LLM Meta AI) family, notable for its open-source release and extensibility toward multilingual, mathematical, and code-oriented tasks. With approximately 33 billion parameters, LLaMA-33B serves as a foundation for research on efficient scaling, adaptation to non-English languages, specialized domain expansion, and hardware-aware deployment techniques.

1. Model Architecture and Tokenization

LLaMA-33B employs a standard transformer architecture with stacked blocks comprising Multi-Head Self-Attention (MHSA), position-wise Feed-Forward Networks (FFNs) with the SwiGLU activation, and RMSNorm normalization. The canonical transformer block is defined as:

• Forward pass:
  1. $x' = x + \text{MHSA}(\text{RMSNorm}(x))$
  2. $y = x' + \text{FFN}(\text{RMSNorm}(x'))$

For multilingual support—specifically Chinese—the tokenizer and vocabulary require substantial adaptation. The original LLaMA tokenizer contained only several hundred Chinese tokens, resulting in suboptimal byte-level tokenization for Chinese text (each character split into multiple tokens). To overcome these limitations:

• A new SentencePiece tokenizer is trained on Chinese corpora, producing a 20,000-token vocabulary.
• This is merged with the original 32,000-token LLaMA vocabulary, yielding a combined Chinese LLaMA tokenizer with 49,953 tokens.
• Model architecture adapts by resizing embedding and projection matrices from $V \times H$ to $V' \times H$, where $V = 32,000$ and $V' = 49,953$, appending new token rows for Chinese.

This extension improves encoding efficiency: the number of tokens required to represent Chinese text is approximately halved, doubling context throughput and inference speed for Chinese content (Cui et al., 2023).

2. Secondary Pre-Training and Task Adaptation

Following vocabulary expansion, LLaMA-33B undergoes secondary pre-training on large-scale Chinese corpora using the causal LLMing objective:

$$\mathcal{L}_{\text{CLM}}(\Theta) = \mathbb{E}_{x \sim \mathcal{D}_{PT}} \left[ - \sum_i \log p(x_i | x_0, x_1, \dots, x_{i-1}; \Theta) \right]$$

This phase shifts the model’s internal representations toward richer Chinese semantic understanding.

Subsequent supervised fine-tuning utilizes instruction datasets derived from Chinese sources, yielding instruction-following variants such as Chinese Alpaca. The prompt template is adapted from the Stanford Alpaca paradigm but optimized for Chinese. This process demonstrably enhances context-aware generation, instruction compliance, and downstream Chinese NLP performance, backed by empirical results on benchmarks such as C-Eval (Cui et al., 2023).

3. Parameter-Efficient Fine-Tuning with LoRA

Given the substantial computational overhead associated with updating all 33B parameters, Low-Rank Adaptation (LoRA) is employed for efficient fine-tuning. LoRA freezes the original parameter matrices and introduces trainable low-rank adapters. For a linear transformation with weight $W_0 \in \mathbb{R}^{d \times k}$, LoRA’s update is:

$h = W_0 x + \Delta W x = W_0 x + B A x$

where $B \in \mathbb{R}^{d \times r}$, $A \in \mathbb{R}^{r \times k}$, and $r \ll \min(d, k)$. LoRA is selectively applied to attention modules and MLP layers, minimizing the trainable parameter count while enabling effective adaption to new domains (language, code, math) (Cui et al., 2023).

In hardware-aware use cases, LoRA weights are frequently stored in high-speed, mutable memory (SRAM), separating them from the fixed base model (Wang et al., 17 Mar 2025).

4. Domain Expansion via Transformer Block Addition

An alternative approach to domain adaptation leverages progressive block expansion (Wu et al., 4 Jan 2024). Rather than fine-tuning existing blocks, new transformer blocks are inserted into the model, initialized as identity functions. Specifically, output projection matrices (MHSA $W^O$, FFN $W_3$) are set to zero, ensuring the new block’s initial operation is $φ_{id}(x) = x$ due to residual connections.

The original blocks are left frozen, and only the expanded blocks are fine-tuned on domain-specific data (e.g., code or mathematics). This strategy increases the model’s capacity for new knowledge while preserving previously learned general skills, mitigating catastrophic forgetting. The process involves:

• Partitioning the transformer stack into groups.
• Inserting identity-initialized blocks between these groups.
• Fine-tuning the new blocks solely with domain data, followed by instruction tuning.

Empirically, this expansion can yield superior performance in general language, code, and mathematical reasoning tasks relative to both the base and code-specialized models, without requiring exhaustive retraining of the entire network (Wu et al., 4 Jan 2024).

5. Hardware-Efficient Deployment through Quantization and Accelerator Design

LLaMA-33B’s large parameter count presents challenges for edge deployment. Recent advances in model quantization and accelerator architecture propose feasible pathways for on-device inference (Wang et al., 17 Mar 2025):

• Quantization reduces model weights from full precision to 4-bit or 2-bit, allowing compression without major accuracy loss.
• ROMA accelerator adopts a hybrid memory layout: quantized base model weights reside in dense ROM, while LoRA adapters and KV cache occupy SRAM.
• The L-Unit in ROMA performs matrix–vector multiplication and dequantization, following:

$$\text{res} = (\text{DotProduct}(\text{value}, w) - \text{vsum} \times z) \times s \times 2^{(\text{max\_exp} - \text{Bias})}$$

• Innovative B-ROM design organizes ROM addresses into blocks, reducing CMOS transistor counts by roughly a factor of 4, while a fused-cell layout combines logic and memory for improved area and power efficiency.

Evaluated with 3B and 8B models, ROMA achieves peak throughputs exceeding 31.8K tokens/s and TTFT latencies as low as 5.6 ms. This suggests that, with similar quantization and architectural optimizations, even LLaMA-33B can be efficiently deployed on-edge, provided sufficient on-chip capacity and robust quantization (Wang et al., 17 Mar 2025).

6. Experimental Benchmarks and Impact

Performance assessments for models with the described adaptations indicate competitive or state-of-the-art results:

• Chinese LLaMA models, after tokenizer and LoRA-enabled adaptation, excel in Chinese understanding and generation, demonstrating strong results in C-Eval across STEM, social sciences, humanities, and other domains—often rivaling much larger models (Cui et al., 2023).
• Block expansion approaches prevent catastrophic forgetting and enable notable gains on programming (HumanEval, MBPP), mathematical reasoning (GSM8K, PoT), and general AI benchmarks (MMLU, ARC), with multi-turn interactions validated via MT-Bench and MINT-Bench agent-centric tests (Wu et al., 4 Jan 2024).
• Quantization-aware on-device inference using ROMA offers token generation speeds orders of magnitude beyond general-purpose CPUs and mainstream GPUs, with implications for real-time, privacy-preserving deployment of LLaMA-33B-scale models (Wang et al., 17 Mar 2025).

7. Future Directions and Plausible Extensions

LLaMA-33B serves as a reference point for ongoing research in scalable NLP model adaptation, efficient multilingual support, safe domain expansion, and hardware-oriented model optimization. Extending tokenizer vocabulary and encoder mechanisms can further improve low-resource language support. Progressive block expansion and parameter-efficient tuning strategies are promising for adapting high-capacity models to new skills without retraining core representations. Memory architecture innovations and quantization strategies increasingly enable deployment of very large models such as LLaMA-33B on resource-constrained devices. A plausible implication is that future language agents will synthesize versatile reasoning (natural language, code, math) and adaptability within strict computational budgets, driven by advances exemplified through the research outputs surveyed above.