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RichardErkhov/liminerity_-_Miniier-blurstral-attempt-1-exl2
liminerity_-_Miniier-blurstral-attempt-1-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.
Miniier-blurstral-attempt-1 - 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/liminerity_-_Miniier-blurstral-attempt-1-exl2 Miniier-blurstral-attempt-1-6_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 liminerity_-_Miniier-blurstral-attempt-1-exl2 --revision 6_5 --local-dir Miniier-blurstral-attempt-1-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download liminerity_-_Miniier-blurstral-attempt-1-exl2 --revision 6_5 --local-dir Miniier-blurstral-attempt-1-6.5 --local-dir-use-symlinks False
license: apache-2.0 tags:
Please note that this model broke during the quantization process but is very very fast. Maybe someone can fine-tune. Miniier-blurstral is a merge of the following models using LazyMergekit:
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 3]
- model: liminerity/Blur-7b-slerp-v0.1
layer_range: [0, 3]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/Miniier-blurstral"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])