rombodawg/Meta-Llama-3.1-8B-Instruct-reuploaded

Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama-3 8b	▶️ Start on Colab	2.4x faster	58% less
Gemma 7b	▶️ Start on Colab	2.4x faster	58% less
Mistral 7b	▶️ Start on Colab	2.2x faster	62% less

| Llama-2 7b | ▶️ Start on Colab | 2.2x faster | 43% less | | TinyLlama | ▶️ Start on Colab | 3.9x faster | 74% less | | CodeLlama 34b A100 | ▶️ Start on Colab | 1.9x faster | 27% less | | Mistral 7b 1xT4 | ▶️ Start on Kaggle | 5x faster* | 62% less | | DPO - Zephyr | ▶️ Start on Colab | 1.9x faster | 19% less |

• This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
• This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
• * Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.