YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Quantization made by Richard Erkhov.
mistral-7b-v0.3 - GGUF
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/mistral-7b-v0.3/
Name | Quant method | Size |
---|---|---|
mistral-7b-v0.3.Q2_K.gguf | Q2_K | 2.54GB |
mistral-7b-v0.3.IQ3_XS.gguf | IQ3_XS | 2.82GB |
mistral-7b-v0.3.IQ3_S.gguf | IQ3_S | 2.97GB |
mistral-7b-v0.3.Q3_K_S.gguf | Q3_K_S | 2.95GB |
mistral-7b-v0.3.IQ3_M.gguf | IQ3_M | 3.06GB |
mistral-7b-v0.3.Q3_K.gguf | Q3_K | 3.28GB |
mistral-7b-v0.3.Q3_K_M.gguf | Q3_K_M | 3.28GB |
mistral-7b-v0.3.Q3_K_L.gguf | Q3_K_L | 3.56GB |
mistral-7b-v0.3.IQ4_XS.gguf | IQ4_XS | 3.68GB |
mistral-7b-v0.3.Q4_0.gguf | Q4_0 | 3.83GB |
mistral-7b-v0.3.IQ4_NL.gguf | IQ4_NL | 3.87GB |
mistral-7b-v0.3.Q4_K_S.gguf | Q4_K_S | 3.86GB |
mistral-7b-v0.3.Q4_K.gguf | Q4_K | 4.07GB |
mistral-7b-v0.3.Q4_K_M.gguf | Q4_K_M | 4.07GB |
mistral-7b-v0.3.Q4_1.gguf | Q4_1 | 4.24GB |
mistral-7b-v0.3.Q5_0.gguf | Q5_0 | 4.66GB |
mistral-7b-v0.3.Q5_K_S.gguf | Q5_K_S | 4.66GB |
mistral-7b-v0.3.Q5_K.gguf | Q5_K | 4.78GB |
mistral-7b-v0.3.Q5_K_M.gguf | Q5_K_M | 4.78GB |
mistral-7b-v0.3.Q5_1.gguf | Q5_1 | 5.07GB |
mistral-7b-v0.3.Q6_K.gguf | Q6_K | 5.54GB |
mistral-7b-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.
- Downloads last month
- 596