metadata

base_model:
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
  - aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
  - Nitral-AI/Hathor_Stable-v0.2-L3-8B
  - Sao10K/L3-8B-Stheno-v3.1
tags:
  - merge
  - mergekit
  - lazymergekit
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
  - aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
  - Nitral-AI/Hathor_Stable-v0.2-L3-8B
  - Sao10K/L3-8B-Stheno-v3.1

L3-Umbral-Mind-RP-v2.0-8B

L3-Umbral-Mind-RP-v2.0-8B is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: Casual-Autopsy/Umbral-Mind-3
  - model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
    parameters:
      weight: 0.04
  - model: aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
    parameters:
      weight: 0.02
  - model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
    parameters:
      weight: 0.02
  - model: Sao10K/L3-8B-Stheno-v3.1
    parameters:
      weight: 0.01
merge_method: task_arithmetic
base_model: Casual-Autopsy/Umbral-Mind-3
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B"
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"])