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ChatGLM3-6B-32K

Parameters

6B

Context Length

33K

Modality

Text

Architecture

Dense

License

ChatGLM3-6B Model License

Release Date

27 Oct 2023

Knowledge Cutoff

-

API Pricing (per 1M)

Self-hosted only

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

14.13 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

32,768 tokens

15.09 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: AbsoluteHidden: 4.1k · Context: 33K · Vocab: 65kx 28 layersRMSNormPre-AttentionMulti-Head Attention32Q / 2KV headsHead dim: 128+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 13.7k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for ChatGLM3-6B-32K available.

Rankings

Overall Rank

-

Coding Rank

-

About ChatGLM3-6B-32K

ChatGLM3-6B-32K is an extended-context variant of ChatGLM3 engineered for long-form document comprehension and multi-turn enterprise dialogue. It utilizes an adjusted base frequency RoPE mechanism to maintain coherence over a 32K context window.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

32

Key-Value Heads

2

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

-

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

4,096

Number of Layers

28

FFN Intermediate Size (Dense)

13,696

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

65,024

About ChatGLM

ChatGLM series models from Z.ai, based on GLM architecture.


Other ChatGLM Models
ChatGLM3-6B-32K: Specifications and GPU VRAM Requirements