OxytocinErosEngineering_v0.1-4x7B-passthrough

OxytocinErosEngineering_v0.1-4x7B-passthrough

OxytocinErosEngineering_v0.1-4x7B-passthrough is a merge of the following models using LazyMergekit:

• ChaoticNeutrals/Eris_Remix_7B
• Virt-io/Erebus-Holodeck-7B
• jeiku/Eros_Prodigadigm_7B
• Epiculous/Mika-7B
• ChaoticNeutrals/Eris_Remix_7B
• Virt-io/Erebus-Holodeck-7B
• jeiku/Eros_Prodigadigm_7B
• Epiculous/Mika-7B
• ChaoticNeutrals/Eris_Remix_7B
• Virt-io/Erebus-Holodeck-7B
• jeiku/Eros_Prodigadigm_7B
• Epiculous/Mika-7B
• ChaoticNeutrals/Eris_Remix_7B
• Virt-io/Erebus-Holodeck-7B
• jeiku/Eros_Prodigadigm_7B
• Epiculous/Mika-7B

🧩 Configuration

slices:
  - sources:
    - model: ChaoticNeutrals/Eris_Remix_7B
      layer_range: [0, 8]
  - sources:
    - model: Virt-io/Erebus-Holodeck-7B
      layer_range: [0, 8]
  - sources:
    - model: jeiku/Eros_Prodigadigm_7B
      layer_range: [0, 8]
  - sources:
    - model: Epiculous/Mika-7B
      layer_range: [0, 8]
  - sources:
    - model: ChaoticNeutrals/Eris_Remix_7B
      layer_range: [8, 16]
  - sources:
    - model: Virt-io/Erebus-Holodeck-7B
      layer_range: [8, 16]
  - sources:
    - model: jeiku/Eros_Prodigadigm_7B
      layer_range: [8, 16]
  - sources:
    - model: Epiculous/Mika-7B
      layer_range: [8, 16]
  - sources:
    - model: ChaoticNeutrals/Eris_Remix_7B
      layer_range: [16, 24]
  - sources:
    - model: Virt-io/Erebus-Holodeck-7B
      layer_range: [16, 24]
  - sources:
    - model: jeiku/Eros_Prodigadigm_7B
      layer_range: [16, 24]
  - sources:
    - model: Epiculous/Mika-7B
      layer_range: [16, 24]
  - sources:
    - model: ChaoticNeutrals/Eris_Remix_7B
      layer_range: [24, 32]
  - sources:
    - model: Virt-io/Erebus-Holodeck-7B
      layer_range: [24, 32]
  - sources:
    - model: jeiku/Eros_Prodigadigm_7B
      layer_range: [24, 32]
  - sources:
    - model: Epiculous/Mika-7B
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "weezywitasneezy/OxytocinErosEngineering_v0.1-4x7B-passthrough"
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"])