Llama-2-7b-gsm8k
This repo contains a dense Llama 2 7B finetuned for arithmetic reasoning task using the GSM8k dataset.
Official model weights from Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment.
Authors: Neural Magic, Cerebras
Usage
Below we share some code snippets on how to get quickly started with running the model.
Running the model
# 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.
Help
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