68 lines
3.0 KiB
Markdown
68 lines
3.0 KiB
Markdown
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---
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base_model: meta-llama/Llama-2-7b-hf
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inference: true
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model_type: llama
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pipeline_tag: text-generation
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datasets:
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- garage-bAInd/Open-Platypus
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- Open-Orca/OpenOrca
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- cognitivecomputations/dolphin
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tags:
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- instruct
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---
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# Llama-2-7b-instruct
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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.
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Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
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**Authors**: Neural Magic, Cerebras
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## Usage
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Below we share some code snippets on how to get quickly started with running the model.
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### Sparse Transfer
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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).
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### Running the model
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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).
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```python
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# pip install transformers accelerate
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-instruct")
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model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-instruct", device_map="auto")
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input_text = "Write a recipe for banana bread:\n"
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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## Evaluation Benchmark Results
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Model evaluation metrics and results.
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| Benchmark | Metric | Llama-2-7b-instruct | Llama-2-7b-pruned50-retrained-instruct |
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|------------------------------------------------|---------------|-------------|-------------------------------|
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| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | xxxx | xxxx |
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| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot | xxxx | xxxx |
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| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | xxxx | xxxx |
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| [ARC-c](https://arxiv.org/abs/1911.01547) | | xxxx | xxxx |
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| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot | xxxx | xxxx |
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| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | xxxx | xxxx |
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| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | xxxx | xxxx |
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## Model Training Details
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Coming soon.
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## Help
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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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