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unsloth_-_codegemma-7b-gguf
RichardErkhov/unsloth_-_codegemma-7b-gguf
unsloth_-_codegemma-7b-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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.gguf116 GB · 100%
At a glance
- Access
- Public
- Created
- Sep 18, 2024
- Updated
- Sep 18, 2024
- SHA
- bbdf0911
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
codegemma-7b - GGUF
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/codegemma-7b/
| Name | Quant method | Size |
|---|---|---|
| codegemma-7b.Q2_K.gguf | Q2_K | 3.24GB |
| codegemma-7b.IQ3_XS.gguf | IQ3_XS | 3.54GB |
| codegemma-7b.IQ3_S.gguf | IQ3_S | 3.71GB |
| codegemma-7b.Q3_K_S.gguf | Q3_K_S | 3.71GB |
| codegemma-7b.IQ3_M.gguf | IQ3_M | 3.82GB |
| codegemma-7b.Q3_K.gguf | Q3_K | 4.07GB |
| codegemma-7b.Q3_K_M.gguf | Q3_K_M | 4.07GB |
| codegemma-7b.Q3_K_L.gguf | Q3_K_L | 4.39GB |
| codegemma-7b.IQ4_XS.gguf | IQ4_XS | 4.48GB |
| codegemma-7b.Q4_0.gguf | Q4_0 | 4.67GB |
| codegemma-7b.IQ4_NL.gguf | IQ4_NL | 4.69GB |
| codegemma-7b.Q4_K_S.gguf | Q4_K_S | 4.7GB |
| codegemma-7b.Q4_K.gguf | Q4_K | 4.96GB |
| codegemma-7b.Q4_K_M.gguf | Q4_K_M | 4.96GB |
| codegemma-7b.Q4_1.gguf | Q4_1 | 5.12GB |
| codegemma-7b.Q5_0.gguf | Q5_0 | 5.57GB |
| codegemma-7b.Q5_K_S.gguf | Q5_K_S | 5.57GB |
| codegemma-7b.Q5_K.gguf | Q5_K | 5.72GB |
| codegemma-7b.Q5_K_M.gguf | Q5_K_M | 5.72GB |
| codegemma-7b.Q5_1.gguf | Q5_1 | 6.02GB |
| codegemma-7b.Q6_K.gguf | Q6_K | 6.53GB |
| codegemma-7b.Q8_0.gguf | Q8_0 | 8.45GB |
Original model description:
language:
- en library_name: transformers license: apache-2.0 tags:
- unsloth
- transformers
- gemma
- bnb
Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for CodeGemma 7b here: https://colab.research.google.com/drive/19lwcRk_ZQ_ZtX-qzFP3qZBBHZNcMD1hh?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/buy%20me%20a%20coffee%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 |
|---|---|---|---|
| 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
- Sep 18, 2024
- Updated
- Sep 18, 2024