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bartowski/Llama-3-8B-Instruct-Coder-exl2
Llama-3-8B-Instruct-Coder-exl2 is a text generation model from bartowski. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20"turboderp's ExLlamaV2 v0.0.20</a for quantization.
Downloads · 30 days
1
7% of all-time downloads
All-time downloads
14
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31.3 GB
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2
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.json1.8 MB · 100%
From the Hugging Face model README
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/rombodawg/Codellama-3-8B-Finetuned-Instruct
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
|---|---|---|---|---|---|---|---|
| 8_0 | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| 6_5 | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, recommended. |
| 5_0 | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| 4_25 | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| 3_5 | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Llama-3-8B-Instruct-Coder-exl2 Llama-3-8B-Instruct-Coder-exl2-6_5
With huggingface hub (credit to TheBloke for instructions):
pip3 install huggingface-hub
To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch:
Linux:
huggingface-cli download bartowski/Llama-3-8B-Instruct-Coder-exl2 --revision 6_5 --local-dir Llama-3-8B-Instruct-Coder-exl2-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download bartowski/Llama-3-8B-Instruct-Coder-exl2 --revision 6_5 --local-dir Llama-3-8B-Instruct-Coder-exl2-6.5 --local-dir-use-symlinks False
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski