2 by fspecii

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  4bit   Autotrain compatible Base model:quantized:unsloth/l... Base model:unsloth/llama-3-8b-...   En   Endpoints compatible   Gguf   Llama   Lora   Quantized   Region:us   Safetensors   Sft   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF ๐Ÿค—: https://huggingface.co/fspecii/2 

2 Benchmarks

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

2 Parameters and Internals

Model Type 
text-generation-inference, transformers, unsloth, llama, trl, sft
Additional Notes 
The model was trained 2x faster compared to traditional methods due to the usage of the Unsloth framework.
Training Details 
Methodology:
finetuned with the Unsloth framework and Huggingface's TRL library
LLM Name2
Repository ๐Ÿค—https://huggingface.co/fspecii/2 
Base Model(s)  Llama 3 8B Bnb 4bit   unsloth/llama-3-8b-bnb-4bit
Model Size8b
Required VRAM16.1 GB
Updated2025-02-22
Maintainerfspecii
Model Files  0.2 GB   5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4   4.9 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf|4bit
Model ArchitectureAutoModelForCausalLM
Licenseapache-2.0
Is Biasednone
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|reserved_special_token_250|>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Moduleso_proj|v_proj|gate_proj|k_proj|up_proj|q_proj|down_proj
LoRA Alpha16
LoRA Dropout0
R Param16

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Llama3 Credpol0K / 21.9 GB150
Llama 3 8B Chat Doctor0K / 16.1 GB80
Llm Nl To Cmd0K / 16.1 GB70
Fine Tuning Bebidas0K / 16.1 GB70
Note: green Score (e.g. "73.2") means that the model is better than fspecii/2.

Rank the 2 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