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TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 - GGUF
- Model creator: https://huggingface.co/habanoz/
- Original model: https://huggingface.co/habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1/
Original model description:
language: - en license: apache-2.0 datasets: - databricks/databricks-dolly-15k pipeline_tag: text-generation base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T model-index: - name: TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 30.55 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 53.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 26.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 35.85 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 58.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=habanoz/TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1 name: Open LLM Leaderboard
TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T finetuned using dolly dataset.
Training took 1 hour on an 'ml.g5.xlarge' instance.
hyperparameters ={
'num_train_epochs': 3, # number of training epochs
'per_device_train_batch_size': 6, # batch size for training
'gradient_accumulation_steps': 2, # Number of updates steps to accumulate
'gradient_checkpointing': True, # save memory but slower backward pass
'bf16': True, # use bfloat16 precision
'tf32': True, # use tf32 precision
'learning_rate': 2e-4, # learning rate
'max_grad_norm': 0.3, # Maximum norm (for gradient clipping)
'warmup_ratio': 0.03, # warmup ratio
"lr_scheduler_type":"constant", # learning rate scheduler
'save_strategy': "epoch", # save strategy for checkpoints
"logging_steps": 10, # log every x steps
'merge_adapters': True, # wether to merge LoRA into the model (needs more memory)
'use_flash_attn': True, # Whether to use Flash Attention
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 34.04 |
| AI2 Reasoning Challenge (25-Shot) | 30.55 |
| HellaSwag (10-Shot) | 53.70 |
| MMLU (5-Shot) | 26.07 |
| TruthfulQA (0-shot) | 35.85 |
| Winogrande (5-shot) | 58.09 |
| GSM8k (5-shot) | 0.00 |
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