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RichardErkhov/SpectraSuite_-_QuantLM_830M_8bit_Unpacked-exl2
SpectraSuite_-_QuantLM_830M_8bit_Unpacked-exl2 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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Updated Jan 18, 2025
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From the Hugging Face model README
Quantization made by Richard Erkhov.
QuantLM_830M_8bit_Unpacked - EXL2
| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/SpectraSuite_-_QuantLM_830M_8bit_Unpacked-exl2 QuantLM_830M_8bit_Unpacked-6_5
With huggingface hub:
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 SpectraSuite_-_QuantLM_830M_8bit_Unpacked-exl2 --revision 6_5 --local-dir QuantLM_830M_8bit_Unpacked-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download SpectraSuite_-_QuantLM_830M_8bit_Unpacked-exl2 --revision 6_5 --local-dir QuantLM_830M_8bit_Unpacked-6.5 --local-dir-use-symlinks False
QuantLM, unpacked to FP16 format - compatible with FP16 GEMMs. After unpacking, QuantLM has the same architecture as LLaMa.
import transformers as tf, torch
model_name = "SpectraSuite/QuantLM_830M_8bit_Unpacked"
# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
pipeline("Once upon a time")