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DopeyGay/DolphinButtocks-7B-slerp
DolphinButtocks-7B-slerp is a machine learning model from DopeyGay. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model has not been tested or evaluated, and its performance, characteristics, and operational status (NOT functional, afaik) are currently unknown. Feedback is appreciated.
Downloads · 30 days
13
39% of all-time downloads
All-time downloads
33
Public
Parameters
7.2B
14.5 GB on disk
Likes
1
Public
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.safetensors14.5 GB · 100%
From the Hugging Face model README
This model has not been tested or evaluated, and its performance, characteristics, and operational status (NOT functional, afaik) are currently unknown. Feedback is appreciated.
AI is a powerful tool, that being said, it may sometimes generate harmful, biased, untrue, or inappropriate content, and this model is no different. Please exercise caution and use it at your own risk, just like you would a drill, or a hammer, or any other tool
DolphinButtocks-7B-slerp is a merge of the following models using LazyMergekit:
slices:
- sources:
- model: TeeZee/Buttocks-7B-v1.1
layer_range: [0, 32]
- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
layer_range: [0, 32]
merge_method: slerp
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
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 = "DopeyGay/DolphinButtocks-7B-slerp"
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"])