base_model: meta-llama/Llama-2-7b-hf
inference: true
model_type: llama
pipeline_tag: text-generation
datasets:
- HuggingFaceH4/ultrachat_200k
tags:
- chat
Llama-2-7b-ultrachat
This repo contains a Llama 2 7B finetuned for chat tasks using the UltraChat 200k 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-ultrachat")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-ultrachat", device_map="auto")
input_text = "Write me a poem about Machine Learning."
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-ultrachat | Llama-2-7b-pruned50-retrained-ultrachat |
|---|---|---|---|
| MMLU | 5-shot, top-1 | xxxx | xxxx |
| HellaSwag | 0-shot | xxxx | xxxx |
| WinoGrande | partial score | xxxx | xxxx |
| ARC-c | xxxx | xxxx | |
| TruthfulQA | 5-shot | xxxx | xxxx |
| HumanEval | pass@1 | xxxx | xxxx |
| GSM8K | maj@1 | xxxx | xxxx |
Model Training Details
Coming soon.
Help
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