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unsloth_-_Meta-Llama-3.1-8B-gguf
RichardErkhov/unsloth_-_Meta-Llama-3.1-8B-gguf
unsloth_-_Meta-Llama-3.1-8B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf108 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 13, 2024
- Updated
- Aug 13, 2024
- SHA
- 4dfd3076
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Meta-Llama-3.1-8B - GGUF
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/Meta-Llama-3.1-8B/
| Name | Quant method | Size |
|---|---|---|
| Meta-Llama-3.1-8B.Q2_K.gguf | Q2_K | 2.96GB |
| Meta-Llama-3.1-8B.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| Meta-Llama-3.1-8B.IQ3_S.gguf | IQ3_S | 3.43GB |
| Meta-Llama-3.1-8B.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| Meta-Llama-3.1-8B.IQ3_M.gguf | IQ3_M | 3.52GB |
| Meta-Llama-3.1-8B.Q3_K.gguf | Q3_K | 3.74GB |
| Meta-Llama-3.1-8B.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| Meta-Llama-3.1-8B.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| Meta-Llama-3.1-8B.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| Meta-Llama-3.1-8B.Q4_0.gguf | Q4_0 | 4.34GB |
| Meta-Llama-3.1-8B.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| Meta-Llama-3.1-8B.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| Meta-Llama-3.1-8B.Q4_K.gguf | Q4_K | 4.58GB |
| Meta-Llama-3.1-8B.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| Meta-Llama-3.1-8B.Q4_1.gguf | Q4_1 | 4.78GB |
| Meta-Llama-3.1-8B.Q5_0.gguf | Q5_0 | 5.21GB |
| Meta-Llama-3.1-8B.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| Meta-Llama-3.1-8B.Q5_K.gguf | Q5_K | 5.34GB |
| Meta-Llama-3.1-8B.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| Meta-Llama-3.1-8B.Q5_1.gguf | Q5_1 | 5.65GB |
| Meta-Llama-3.1-8B.Q6_K.gguf | Q6_K | 6.14GB |
| Meta-Llama-3.1-8B.Q8_0.gguf | Q8_0 | 7.95GB |
Original model description:
language:
- en library_name: transformers license: llama3.1 tags:
- llama-3
- llama
- meta
- unsloth
- transformers
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
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/> <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
✨ 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.
- Created
- Aug 13, 2024
- Updated
- Aug 13, 2024