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Model: neuralmagic/Llama-2-7b-evol-code-alpaca-pruned_70
Source: Original Platform

README.md

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+---
+base_model: neuralmagic/Llama-2-7b-pruned70-retrained
+inference: true
+model_type: llama
+pipeline_tag: text-generation
+datasets:
+  - cerebras/SlimPajama-627B
+  - theblackcat102/evol-codealpaca-v1
+tags:
+- sparse
+- code
+---
+
+# Llama-2-7b-pruned70-retrained-evolcodealpaca
+
+This repo contains a [70% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) finetuned for code generation tasks using the [Evolved CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.
+
+Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
+
+**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](https://neuralmagic.github.io/docs-v2/get-started/transfer).
+
+### Running the model
+
+This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained-evolcodealpaca")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned70-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-pruned70-retrained-evolcodealpaca |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | 32.03       | 36.3                          |
+
+## Model Training Details
+
+This model was obtained by gradual sparse-transfer of the sparse foundational model [Llama-2-7b-pruned50-retrained](https://huggingface.co/neuralmagic/Llama-2-7b-pruned50-retrained) on 60% of the [evolcodealpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.
+The 50% sparse foundational model was finetuned for 2 epochs and then pruned to 70% sparsity using [SparseGPT](https://arxiv.org/abs/2301.00774). Then, the model was finetuned for 1 more epoch with the [SquareHead](https://arxiv.org/abs/2310.06927) knowledge distillation with [Llama-2-7b-evolcodealpaca](https://huggingface.co/neuralmagic/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](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)