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ilevytate/Purr-slerp-tual-7B
Purr-slerp-tual-7B is a machine learning model from ilevytate. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Purr-slerp-tual-7B is a merge of the following models using LazyMergekit: diffnamehard/Mistral-CatMacaroni-slerp-uncensored-7B liminerity/Blured-Ties-7B
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Updated Mar 29, 2024
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From the Hugging Face model README
Purr-slerp-tual-7B is a merge of the following models using LazyMergekit:
slices:
- sources:
- model: diffnamehard/Mistral-CatMacaroni-slerp-uncensored-7B
layer_range: [0, 40]
- model: liminerity/Blured-Ties-7B
layer_range: [0, 40]
# or, the equivalent models: syntax:
# models:
# - model: diffnamehard/Mistral-CatMacaroni-slerp-uncensored-7B
# - model: liminerity/Blured-Ties-7B
merge_method: slerp
base_model: diffnamehard/Mistral-CatMacaroni-slerp-uncensored-7B
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 # fallback for rest of tensors
dtype: float16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ilevytate/Purr-slerp-tual-7B"
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"])