unsloth_-_mistral-7b-instruct-v0.3-gguf

RichardErkhov/unsloth_-_mistral-7b-instruct-v0.3-gguf

unsloth_-_mistral-7b-instruct-v0.3-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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82.3 GB

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.gguf95.3 GB · 100%

At a glance

Access: Public
Created: Jun 25, 2024
Updated: Jun 25, 2024
SHA: 9610db48

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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mistral-7b-instruct-v0.3 - GGUF

Model creator: https://huggingface.co/unsloth/
Original model: https://huggingface.co/unsloth/mistral-7b-instruct-v0.3/

Name	Quant method	Size
mistral-7b-instruct-v0.3.Q2_K.gguf	Q2_K	2.54GB
mistral-7b-instruct-v0.3.IQ3_XS.gguf	IQ3_XS	2.82GB
mistral-7b-instruct-v0.3.IQ3_S.gguf	IQ3_S	2.97GB
mistral-7b-instruct-v0.3.Q3_K_S.gguf	Q3_K_S	2.95GB
mistral-7b-instruct-v0.3.IQ3_M.gguf	IQ3_M	3.06GB
mistral-7b-instruct-v0.3.Q3_K.gguf	Q3_K	3.28GB
mistral-7b-instruct-v0.3.Q3_K_M.gguf	Q3_K_M	3.28GB
mistral-7b-instruct-v0.3.Q3_K_L.gguf	Q3_K_L	3.56GB
mistral-7b-instruct-v0.3.IQ4_XS.gguf	IQ4_XS	3.68GB
mistral-7b-instruct-v0.3.Q4_0.gguf	Q4_0	3.83GB
mistral-7b-instruct-v0.3.IQ4_NL.gguf	IQ4_NL	3.87GB
mistral-7b-instruct-v0.3.Q4_K_S.gguf	Q4_K_S	3.86GB
mistral-7b-instruct-v0.3.Q4_K.gguf	Q4_K	4.07GB
mistral-7b-instruct-v0.3.Q4_K_M.gguf	Q4_K_M	4.07GB
mistral-7b-instruct-v0.3.Q4_1.gguf	Q4_1	4.24GB
mistral-7b-instruct-v0.3.Q5_0.gguf	Q5_0	4.66GB
mistral-7b-instruct-v0.3.Q5_K_S.gguf	Q5_K_S	4.66GB
mistral-7b-instruct-v0.3.Q5_K.gguf	Q5_K	4.78GB
mistral-7b-instruct-v0.3.Q5_K_M.gguf	Q5_K_M	4.78GB
mistral-7b-instruct-v0.3.Q5_1.gguf	Q5_1	5.07GB
mistral-7b-instruct-v0.3.Q6_K.gguf	Q6_K	5.54GB
mistral-7b-instruct-v0.3.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language:

en license: apache-2.0 library_name: transformers tags:
unsloth
transformers
mistral
mistral-7b
mistral-instruct
instruct

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

We have a Google Colab Tesla T4 notebook for Mistral v3 7b here: https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing

For conversational ShareGPT style and using Mistral v3 Instruct: https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?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
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: Jun 25, 2024
Updated: Jun 25, 2024