Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.48 |
| IFEval (0-Shot) | 54.06 |
| BBH (3-Shot) | 39.88 |
| MATH Lvl 5 (4-Shot) | 18.73 |
| GPQA (0-shot) | 5.82 |
| MuSR (0-shot) | 9.95 |
| MMLU-PRO (5-shot) | 42.42 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard54.060
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard39.880
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard18.730
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.820
- acc_norm on MuSR (0-shot)Open LLM Leaderboard9.950
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard42.420