# pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-gsm8k")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-gsm8k", device_map="auto")

input_text = "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"
input_ids = tokenizer.apply_chat_template(input_text, add_generation_prompt=True, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Evaluation Benchmark Results

Model evaluation metrics and results.

Benchmark	Metric	Llama-2-7b-gsm8k
GSM8K	0-shot	35.5%

Model Training Details

This model was obtained by fine-tuning the dense Llama 2 7B on the GSM8k dataset. Fine-tuning was performed for 2 epochs with batch-size of 32, with linearly decaying learning-rate from initial value of 3e-5 and warm-up phase of 20 steps.

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RedHatAI/Llama-2-7b-gsm8k

Llama-2-7b-gsm8k

Usage

Running the model

Evaluation Benchmark Results

Model Training Details

Help

RedHatAI/
Llama-2-7b-gsm8k