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---
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.
```python
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-2T-lr-2e-4-3ep-dolly-15k-instruct-v1)
| 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|
|