How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mergekit-community/because-im-bored-nsfw2-linear")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mergekit-community/because-im-bored-nsfw2-linear")
model = AutoModelForCausalLM.from_pretrained("mergekit-community/because-im-bored-nsfw2-linear", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mergekit-community/because_im_bored_nsfw1+kik41/lora-type-descriptive-llama-3-8b-v2 
    parameters:
      weight: 1.0
  - model: mergekit-community/because_im_bored_nsfw1+kik41/lora-length-long-llama-3-8b-v2
    parameters:
      weight: 1.0
  - model: mergekit-community/because_im_bored_nsfw1+Azazelle/Llama-3-LongStory-LORA
    parameters:
      weight: 1.0
merge_method: linear
dtype: float32
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