Falcon-7B
Parameters
7B
Context Length
2K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
5 Jun 2023
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
System Requirements
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
2,048 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Falcon-7B available.
Rankings
Overall Rank
-
Coding Rank
-
About Falcon-7B
Falcon-7B is an open-source 7B parameter causal language model developed by TII for text generation, conversational AI, and summarization. Trained on 1.5T tokens with Multi-Query Attention, it provides fast, accessible inference under Apache 2.0.
Technical Specifications
Attention
Attention Structure
Multi-Query Attention
Attention Heads
71
Key-Value Heads
1
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
-
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
Layer Normalization
Activation Function
-
Dimensions
Hidden Dimension Size
4,544
Number of Layers
32
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
65,024
About Falcon
The TII Falcon model family comprises causal decoder-only language models (7B, 40B). Their architecture, adapted from GPT-3, integrates rotary positional embeddings, Multi-Query Attention for inference efficiency, and FlashAttention for accelerated operations. Models are trained on the RefinedWeb dataset.