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Mallam 3B 4096 by mesolitica

 »  All LLMs  »  mesolitica  »  Mallam 3B 4096   URL Share it on

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  Autotrain compatible   Endpoints compatible   Mistral   Ms   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/mesolitica/mallam-3B-4096 

Mallam 3B 4096 Benchmarks

LLME Score: 0.14709
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mallam 3B 4096 (mesolitica/mallam-3B-4096)

Mallam 3B 4096 Parameters and Internals

Model Type 
text generation
Additional Notes 
MaLLaM is specifically focused on Malaysian text, leveraging the Mistral architecture and fine-tuned for efficient text generation.
Supported Languages 
Malay (native)
Training Details 
Data Sources:
https://github.com/malaysia-ai/dedup-text-dataset/tree/main/pretrain-llm
Data Volume:
90B tokens
Context Length:
4096
Hardware Used:
5 nodes of 4x A100 80GB
Model Architecture:
Mistral
Input Output 
Input Format:
text
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Use flash attention and 4-bit quantization for optimal performance.
LLM NameMallam 3B 4096
Repository 🤗https://huggingface.co/mesolitica/mallam-3B-4096 
Model Size3b
Required VRAM6.1 GB
Updated2025-02-22
Maintainermesolitica
Model Typemistral
Model Files  5.0 GB: 1-of-2   1.1 GB: 2-of-2
Supported Languagesms
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.36.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<pad>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than mesolitica/mallam-3B-4096.

Rank the Mallam 3B 4096 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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