CroissantLLM Base
Parameters
1.3B
Context Length
2K
Modality
Text
Architecture
Dense
License
Apache-2.0
Release Date
29 Feb 2024
Knowledge Cutoff
Nov 2023
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 CroissantLLM Base available.
Rankings
Overall Rank
-
Coding Rank
-
About CroissantLLM Base
CroissantLLM Base is a 1.3B parameter bilingual model trained with a strictly balanced 1:1 ratio of French and English tokens. Built on the Llama architecture with a custom tokenizer, it is optimized for high-performance deployment on consumer devices.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
16
Key-Value Heads
16
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
10,000
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
2,048
Number of Layers
24
FFN Intermediate Size (Dense)
5,504
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
32,000
About CroissantLLM
CroissantLLM is a bilingual French-English language model developed by French research institutions. The model is trained on a curated mix of French and English data to provide language understanding while preserving French linguistic heritage. It is designed for low-resource inference on consumer-grade hardware.
Other CroissantLLM Models
- No related models available