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Tinyllama Chat Bnb 4bit by unsloth

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Model Card on HF 🤗: https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit 

Tinyllama Chat Bnb 4bit Benchmarks

LLME Score: 0.20901
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Tinyllama Chat Bnb 4bit (unsloth/tinyllama-chat-bnb-4bit)

Tinyllama Chat Bnb 4bit Parameters and Internals

Model Type 
text generation, fine-tuning
Input Output 
Performance Tips:
All notebooks are beginner friendly. Add your dataset, click 'Run All', and obtain a 2x faster finetuned model.
LLM NameTinyllama Chat Bnb 4bit
Repository 🤗https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit 
Model Size630.9m
Required VRAM0.8 GB
Updated2024-12-14
Maintainerunsloth
Model Typellama
Model Files  0.8 GB
Supported Languagesen
Quantization Type4bit
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.44.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

Quantized Models of the Tinyllama Chat Bnb 4bit

Model
Likes
Downloads
VRAM
Tinyllama Coder Py V1305770 GB
Tinyllama Coder Py V1205470 GB
Tinyllama Coder Py 4bit V1001300 GB
Tinyllama Coder Py 4bit V301031 GB
Tinyllama Coder Py V140192 GB
Tinyllama Coder Py 4bit V40221 GB
TinyLlama QuantumQuill Chat0262 GB
...a QuantumQuill Chat 08 05 24 20192 GB
...icipal Prediction Merged Model0342 GB
TinyLlama Indo Chat V.01082 GB

Best Alternatives to Tinyllama Chat Bnb 4bit

Best Alternatives
Context / RAM
Downloads
Likes
Tinyllama4K / 0.8 GB80
Merged 4bit Tiny Llama Gc24K / 0.8 GB120
Tinyllama Singlish GPT2K / 0.8 GB70
Jenna Tiny 2.02K / 0.8 GB80
Tinyllama Bnb 4bit2K / 0.8 GB48289
Palmer 003 Turbo 24012K / 0.8 GB151
Palmer 003 Turbo2K / 0.8 GB171
MCQV3 Q2K / 0.8 GB80
V11Q2K / 0.8 GB60
Note: green Score (e.g. "73.2") means that the model is better than unsloth/tinyllama-chat-bnb-4bit.

Rank the Tinyllama Chat Bnb 4bit Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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