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README.md
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It achieves the following results on the evaluation set:
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- Loss: 0.3878
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## Model description
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More information needed
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- Datasets 3.0.1
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- Tokenizers 0.13.3
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## Use this model
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```
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from transformers import AutoModelForCausalLM
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from transformers import BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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)
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model = AutoModelForCausalLM.from_pretrained(
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"hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp",
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quantization_config=quantization_config,
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torch_dtype=torch.bfloat16,
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)
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```
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It achieves the following results on the evaluation set:
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- Loss: 0.3878
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Test set:
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- BLEU: 65.06
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- COMET: 89.13
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- CodeBLEU: 78.52
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- N-gram match score: 66.81
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- Weighted n-gram match score: 82.49
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- Syntax match score: 75.77
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- Dataflow match score: 89.02
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## Model description
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More information needed
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- Datasets 3.0.1
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- Tokenizers 0.13.3
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```
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