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. | 18.40 |
IFEval (0-Shot) | 21.42 |
BBH (3-Shot) | 28.46 |
MATH Lvl 5 (4-Shot) | 12.54 |
GPQA (0-shot) | 9.28 |
MuSR (0-shot) | 9.04 |
MMLU-PRO (5-shot) | 29.63 |
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the model is not deployed on the HF Inference API.
Model tree for abhishek/autotrain-vr4a1-e5mms
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct
Dataset used to train abhishek/autotrain-vr4a1-e5mms
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard21.420
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard28.460
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard12.540
- acc_norm on GPQA (0-shot)Open LLM Leaderboard9.280
- acc_norm on MuSR (0-shot)Open LLM Leaderboard9.040
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard29.630