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="NexesMess/Llama_3.x_70b_Doppelectra_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("NexesMess/Llama_3.x_70b_Doppelectra_v2")
model = AutoModelForMultimodalLM.from_pretrained("NexesMess/Llama_3.x_70b_Doppelectra_v2")
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=40)
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 Arcee Fusion merge method using nbeerbower/Llama3.1-Gutenberg-Doppel-70B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: arcee_fusion
models:
  - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
    parameters:
      weight: 1.0
  - model: Steelskull/L3.3-Electra-R1-70b
    parameters:
      weight: 1.0
base_model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
dtype: float32
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
chat_template: auto
tokenizer:
  source: union
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