Parameters
1B
Context Length
8K
Modality
Text
Architecture
Dense
License
TII Falcon-LLM License 2.0
Release Date
17 Dec 2024
Knowledge Cutoff
-
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
8,192 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
No evaluation benchmarks for Falcon3-1B available.
Overall Rank
-
Coding Rank
-
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.
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
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.
APX AI
Online