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Model: neuralmagic/Llama-2-7b-dolphin-open_platypus
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---
base_model: meta-llama/Llama-2-7b-hf
inference: true
model_type: llama
pipeline_tag: text-generation
datasets:
- garage-bAInd/Open-Platypus
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin
tags:
- instruct
---
# Llama-2-7b-instruct
This repo contains a [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) finetuned for instruction-following tasks using a blend of the Platypus + Open Orca + Dolphin datasets.
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-instruct")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-instruct", device_map="auto")
input_text = "Write a recipe for banana bread:\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-instruct | Llama-2-7b-pruned50-retrained-instruct |
|------------------------------------------------|---------------|-------------|-------------------------------|
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | xxxx | xxxx |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot | xxxx | xxxx |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | xxxx | xxxx |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | xxxx | xxxx |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot | xxxx | xxxx |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | xxxx | xxxx |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | xxxx | xxxx |
## Model Training Details
Coming soon.
## 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)

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{
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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