base_model: neuralmagic/Llama-2-7b-pruned50-retrained
inference: true
model_type: llama
pipeline_tag: text-generation
datasets:
- cerebras/SlimPajama-627B
- theblackcat102/evol-codealpaca-v1
tags:
- sparse
- code
Llama-2-7b-pruned50-retrained-evolcodealpaca
This repo contains a 50% sparse Llama 2 7B finetuned for code generation tasks using the Evolved CodeAlpaca dataset.
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-pruned50-retrained-evolcodealpaca")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained-evolcodealpaca", device_map="auto")
input_text = "def fibonacci(n):\n"
input_ids = tokenizer(input_text, 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-evolcodealpaca | Llama-2-7b-pruned50-retrained-evolcodealpaca |
|---|---|---|---|
| HumanEval | pass@1 | 32.03 | 32.7 |
Model Training Details
This model was obtained by sparse-tranfer of the sparse foundational model Llama-2-7b-pruned50-retrained on 60% of the evolcodealpaca dataset. Training was perfomerd for 2 epochs and used the SquareHead knowledge distillation with Llama-2-7b-evolcodealpaca as teacher.
Help
For further support, and discussions on these models and AI in general, join Neural Magic's Slack Community