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README.md

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+---
+datasets:
+- openai/gsm8k
+language:
+- en
+metrics:
+- accuracy
+base_model: meta-llama/Llama-2-7b-hf
+inference: true
+model_type: llama
+pipeline_tag: text-generation
+---
+
+# Llama-2-7b-gsm8k
+
+This repo contains a [dense Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) finetuned for arithmetic reasoning task using the [GSM8k](https://huggingface.co/datasets/openai/gsm8k) 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.
+
+### Running the model
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-gsm8k")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-gsm8k", device_map="auto")
+
+input_text = "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"
+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-gsm8k |
+|------------------------------------------------|---------------|-------------|
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | 0-shot        | 35.5%       | 
+
+
+## Model Training Details
+
+sp0_2ep_lr3e-5_bs32_warmup20ba
+
+This model was obtained by fine-tuning the [dense Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the [GSM8k](https://huggingface.co/datasets/openai/gsm8k) dataset.
+Fine-tuning was performed for 2 epochs with batch-size of 32, with linearly decaying learning-rate from initial value of 3e-5 and warm-up phase of 20 steps.
+
+## 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)
+
+