metadata

base_model:
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
tags:
  - merge
  - mergekit
  - lazymergekit
  - Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B

Llama3-UmbralMind-v1-15M

Llama3-UmbralMind-v1-15M is a merge of the following models using LazyMergekit:

🧩 Configuration

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 24]
    model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- sources:
  - layer_range: [8, 24]
    model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [8, 24]
    model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [24, 32]
    model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Tremontaine/Llama3-UmbralMind-v1-15M"
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"])