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Fastchat T5 3B V1.0 Ct2 Int8 by jncraton

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Model Card on HF 🤗: https://huggingface.co/jncraton/fastchat-t5-3b-v1.0-ct2-int8 

Fastchat T5 3B V1.0 Ct2 Int8 Benchmarks

LLME Score: 0.11242
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fastchat T5 3B V1.0 Ct2 Int8 (jncraton/fastchat-t5-3b-v1.0-ct2-int8)

Fastchat T5 3B V1.0 Ct2 Int8 Parameters and Internals

Model Type 
chatbot, text generation
Use Cases 
Primary Use Cases:
Commercial usage of large language models, Chatbot for various purposes, Research purposes
Training Details 
Data Sources:
ShareGPT.com
Data Volume:
70K conversations
Methodology:
The model is fine-tuned on user-shared conversations collected from ShareGPT. It uses the encoder-decoder transformer architecture where the encoder bi-directionally encodes a question and the decoder generates an answer.
Model Architecture:
Encoder-decoder transformer architecture. The encoder bi-directionally encodes questions while the decoder utilizes cross-attention and autoregressively generates answers.
LLM NameFastchat T5 3B V1.0 Ct2 Int8
Repository 🤗https://huggingface.co/jncraton/fastchat-t5-3b-v1.0-ct2-int8 
Model Size3b
Required VRAM2.9 GB
Updated2025-02-22
Maintainerjncraton
Model Files  2.9 GB
Model ArchitectureAutoModel
Licenseapache-2.0
Model Max Length512
Tokenizer ClassT5Tokenizer
Padding Token<pad>

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Note: green Score (e.g. "73.2") means that the model is better than jncraton/fastchat-t5-3b-v1.0-ct2-int8.

Rank the Fastchat T5 3B V1.0 Ct2 Int8 Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241227
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