Llama-2-7b-gsm8k-pruned_70
This repo contains a 70% sparse Llama 2 7B finetuned for arithmetic reasoning 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.
Sparse Transfer
By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process here.
Running the model
This model may be run with the transformers library. For accelerated inference with sparsity, deploy with nm-vllm or deepsparse.
# pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-gsm8k-pruned_70")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-gsm8k-pruned_70", 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 | Llama-2-7b-gsm8k-pruned_70 |
|---|---|---|---|
| GSM8K | 0-shot | 35.5% | 34.3% |
Model Training Details
This model was obtained by sparse-tranfer of the sparse foundational model Llama-2-7b-pruned70-retrained on the GSM8k dataset. Sparse-transfer was performed with SquareHead knowledge distillation with Llama-2-7b-gsm8k as teacher.
Help
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