
# Pythia 1.4b Deduped with 8k Context Window

This model fine-tunes Pythia 1.4b model with a context window of 8k tokens. With optimizations like Flash Attention & bitsandbytes, I could fit the model the entire model with a batch size of 1, on a single A100 (40 GB). The fine-tuning took ~30 hours, after which the loss was similar to that of fine-tuning at the context window of 2k tokens.
{"library_name": "transformers", "datasets": ["EleutherAI/the_pile_deduplicated"], "pipeline_tag": "text-generation"}
transformers pytorch safetensors gpt_neox text-generation dataset:EleutherAI/the_pile_deduplicated autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Pythia 1.4b Deduped with 8k Context Window

This model fine-tunes Pythia 1.4b model with a context window of 8k tokens. With optimizations like Flash Attention & bitsandbytes, I could fit the model the entire model with a batch size of 1, on a single A100 (40 GB). The fine-tuning took ~30 hours, after which the loss was similar to that of fine-tuning at the context window of 2k tokens.
# Pythia 1.4b Deduped with 8k Context Window

This model fine-tunes Pythia 1.4b model with a context window of 8k tokens. With optimizations like Flash Attention & bitsandbytes, I could fit the model the entire model with a batch size of 1, on a single A100 (40 GB). The fine-tuning took ~30 hours, after which the loss was similar to that of fine-tuning at the context window of 2k tokens.
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Pythia 1.4b Deduped with 8k Context Window

This model fine-tunes Pythia 1.4b model with a context window of 8k tokens. With optimizations like Flash Attention & bitsandbytes, I could fit the model the entire model with a batch size of 1, on a single A100 (40 GB). The fine-tuning took ~30 hours, after which the loss was similar to that of fine-tuning at the context window of 2k tokens.
TAGS
#transformers #pytorch #safetensors #gpt_neox #text-generation #dataset-EleutherAI/the_pile_deduplicated #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Pythia 1.4b Deduped with 8k Context Window

This model fine-tunes Pythia 1.4b model with a context window of 8k tokens. With optimizations like Flash Attention & bitsandbytes, I could fit the model the entire model with a batch size of 1, on a single A100 (40 GB). The fine-tuning took ~30 hours, after which the loss was similar to that of fine-tuning at the context window of 2k tokens.