codellama-7b-humaneval-java-fim

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codellama-7b-humaneval-java-fim

This model was trained from scratch on an this dataset for FIM task. It achieves the following results on the evaluation set:

• Loss: 0.6155

Model description

Codellama-7b model trained for FIM on Java code dataset.

Intended uses & limitations

Bleh

Training and evaluation data

Dataset mentioned above

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 30
• training_steps: 2000

Training results

Framework versions

• PEFT 0.5.0
• Transformers 4.34.0
• Pytorch 2.1.0+cu118
• Datasets 2.16.1
• Tokenizers 0.14.1