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PyLlama 7B by mlabonne

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  Autotrain compatible Dataset:mlabonne/evol-instruct...   Endpoints compatible   Instruct   Llama   Lora   Pytorch   Region:us   Safetensors   Sharded
Model Card on HF 🤗: https://huggingface.co/mlabonne/PyLlama-7b 

PyLlama 7B Benchmarks

LLME Score: 0.12378
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
PyLlama 7B (mlabonne/PyLlama-7b)

PyLlama 7B Parameters and Internals

Model Type 
text generation
Additional Notes 
The model is fine-tuned using QLoRA with 4-bit precision on the Evol-Instruct-Python-26k dataset. The entire training is done on an RTX 3090 card.
Training Details 
Data Sources:
mlabonne/Evol-Instruct-Python-26k
Methodology:
Fine-tuning using QLoRA (4-bit precision)
Context Length:
2048
Training Time:
9h 52m 34s
Hardware Used:
RTX 3090
Model Architecture:
LlamaForCausalLM
LLM NamePyLlama 7B
Repository 🤗https://huggingface.co/mlabonne/PyLlama-7b 
Model Size7b
Required VRAM13.5 GB
Updated2025-03-13
Maintainermlabonne
Instruction-BasedYes
Model Files  0.2 GB   10.0 GB: 1-of-2   3.5 GB: 2-of-2   0.0 GB
Model ArchitectureAutoModelForCausalLM
Licenseapache-2.0
Is Biasednone
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesk_proj|v_proj|o_proj|gate_proj|down_proj|up_proj|q_proj
LoRA Alpha16
LoRA Dropout0.05
R Param32

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Note: green Score (e.g. "73.2") means that the model is better than mlabonne/PyLlama-7b.

Rank the PyLlama 7B 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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