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Falcon3-1B

Parameters

1B

Context Length

8K

Modality

Text

Architecture

Dense

License

TII Falcon-LLM License 2.0

Release Date

17 Dec 2024

Knowledge Cutoff

-

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

3.68 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

8,192 tokens

4.23 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 768 · Context: 8K · Vocab: 131.1kx 18 layersRMSNormPre-AttentionGrouped-Query Attention16Q / 4KV headsHead dim: 256+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 8.2k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for Falcon3-1B available.

Rankings

Overall Rank

-

Coding Rank

-

About Falcon3-1B

The Falcon3-1B model is a member of the Falcon 3 family of decoder-only large language models, developed by the Technology Innovation Institute (TII). This family of models emphasizes enhancing capabilities in scientific, mathematical, and coding domains, while maintaining a strong focus on training efficiency. The Falcon3-1B variant is specifically engineered to operate effectively on lightweight computational infrastructures, including devices such as laptops, thereby broadening the accessibility of advanced AI capabilities. It supports multilingual applications, including English, French, Spanish, and Portuguese.

Architecturally, Falcon3-1B is built upon a Transformer-based causal decoder-only design, incorporating 18 decoder blocks. The model utilizes Grouped Query Attention (GQA), configured with 8 query heads and 4 key-value heads, which contributes to efficient inference by minimizing memory consumption for the Key-Value (KV) cache. For activation, the model employs SwiGLU, and for normalization, it integrates RMSNorm. Positional embeddings are handled via Rotary Position Embeddings (RoPE), facilitating effective long-context understanding. The tokenizer for Falcon3-1B supports an extensive vocabulary of 131,000 tokens, which aids in data compression and downstream performance. Furthermore, the architecture incorporates Flash Attention 2 for optimized computational throughput.

Falcon3-1B is designed for a variety of natural language processing tasks, including but not limited to reasoning, language comprehension, instruction following, code generation, and mathematical problem-solving. Its design allows for its deployment in generative AI applications and conversational AI systems. The model's efficiency and optimized variants, such as quantized versions, enable its use in environments with constrained resources, providing a practical solution for diverse real-world applications.

Technical Specifications

Attention

Attention Structure

Grouped-Query Attention

Attention Heads

16

Key-Value Heads

4

Attention Head Dimension

256

Position Embedding

ROPE

RoPE Theta

1,000,042

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

Linear Attention

-

Linear Attention Ratio

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

768

Number of Layers

18

FFN Intermediate Size (Dense)

8,192

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

131,072

About Falcon 3

The TII Falcon 3 model family comprises open-source, decoder-only language models (1B-10B parameters) designed for efficiency. Key innovations include an extended 32K token context window, Grouped-Query Attention (GQA), and specialized versions for scientific and code-oriented applications. Some variants integrate Mamba-based architectures.


Other Falcon 3 Models
Falcon3-1B: Specifications and GPU VRAM Requirements