Mistral-7B-v0.1
Parameters
7.3B
Context Length
8K
Modality
Text
Architecture
Dense
KV Cache Rate
128 KB/tok
License
Apache 2.0
Release Date
27 Sept 2023
Knowledge Cutoff
Aug 2021
API Pricing (per 1M)
Self-hosted only
Hardware & System Requirements
Precision
Context
Workload Memory: 6.90 GB
INT4 · 8K context
Base Weights: 3.40 GB
KV Cache: 1.00 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
1x NVIDIA L4 24GB
6.9 / 21.6 GB usable
1x RTX 3060 12GB
6.9 / 10.8 GB usable
Apple M4 24GB
6.9 / 18.0 GB usable
Determine requirements for your specific workload:
Open in VRAM Calculator
Runtime requirements
Checkpoint format
FP16
CUDA
>= 11.8
PyTorch
>= 2.1
Transformers
>= 4.34.0.dev0
| Architecture | Precision | Attention kernel |
|---|---|---|
Blackwell, Hopper | Native FP16 | FlashAttention-3 |
Ada Lovelace, Ampere | Native FP16 | FlashAttention-2 |
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Mistral-7B-v0.1 available.
Rankings
Overall Rank
-
Coding Rank
-
About Mistral-7B-v0.1
Mistral-7B-v0.1 is a 7.3 billion parameter large language model developed by Mistral AI, engineered for superior performance and computational efficiency in natural language processing tasks. Its design prioritizes efficient inference, making it suitable for practical deployment across various applications. The model is built upon a decoder-only transformer architecture, integrating several key innovations to optimize its operation.
Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
32
Key-Value Heads
8
Position Embedding
ROPE
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
4,096
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Base Precision
FP16
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
14,336
Tokenizer
Vocabulary Size
32,000
About Mistral 7B
Mistral 7B, a 7.3 billion parameter model, utilizes a decoder-only transformer architecture. It features Sliding Window Attention and Grouped Query Attention for efficient long sequence processing. A Rolling Buffer Cache optimizes memory use, contributing to its design for efficient language processing.