CataLlama V0.2 Instruct SFT by catallama

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  Autotrain compatible Base model:catallama/catallama... Base model:finetune:catallama/...   Ca   Catalan   Conversational Dataset:catallama/catalan-inst...   En   Endpoints compatible   Instruct   Llama   Llama-3   Region:us   Safetensors   Sharded   Tensorflow

CataLlama V0.2 Instruct SFT Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CataLlama V0.2 Instruct SFT (catallama/CataLlama-v0.2-Instruct-SFT)

CataLlama V0.2 Instruct SFT Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
Commercial, Research
Applications:
Assistant-like chat, Natural language generation tasks
Primary Use Cases:
Information extraction, Named Entity Recognition, Translation, Summarization, Sentiment analysis, Chat
Limitations:
Not designed to beat benchmarks, Limited to English and Catalan, Check Llama 3 Community License for restrictions
Supported Languages 
ca (Catalan), en (English)
Training Details 
Data Sources:
catallama/Catalan-Instruct-V2
Data Volume:
620 million tokens
Methodology:
Instruction fine-tuning
Hardware Used:
8x A100 80GB GPUs
Model Architecture:
Auto-regressive language model using optimized transformer architecture
Input Output 
Input Format:
Same prompt template as Llama-3 Instruct
Accepted Modalities:
text
Output Format:
Generated text in specified language
LLM NameCataLlama V0.2 Instruct SFT
Repository ๐Ÿค—https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT 
Base Model(s)  catallama/CataLlama-v0.2-Base   catallama/CataLlama-v0.2-Base
Model Size8b
Required VRAM16.1 GB
Updated2025-02-05
Maintainercatallama
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4   0.0 GB
Supported Languagesca en
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|eot_id|>
Vocabulary Size128256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than catallama/CataLlama-v0.2-Instruct-SFT.

Rank the CataLlama V0.2 Instruct SFT 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227