---
license: llama3.3
datasets:
- allenai/c4
language:
- en
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---


# Overview
This document presents the evaluation results of `DeepSeek-R1-Distill-Llama-70B`, a **4-bit quantized model using GPTQ**, evaluated with the **Language Model Evaluation Harness** on the **ARC-Challenge** benchmark.


## ⚙️ Model Configuration

- **Model:** `Llama-3.3-70B-Instruct`
- **Parameters:** `70 billion`
- **Quantization:** `4-bit GPTQ`
- **Source:** Hugging Face (`hf`)
- **Precision:** `torch.float16`
- **Hardware:** `NVIDIA A100 80GB PCIe`
- **CUDA Version:** `12.4`
- **PyTorch Version:** `2.6.0+cu124`
- **Batch Size:** `1`


📌 **Interpretation:**
- The evaluation was performed on a **high-performance GPU (A100 80GB)**.
- The model is significantly larger than the previous 8B version, with **GPTQ 4-bit quantization reducing memory footprint**.
- A **single-sample batch size** was used, which might slow evaluation speed.

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📌 Let us know if you need further analysis or model tuning! 🚀