metadata

base_model:
  - SvalTek/L3-ColdBrew-Astrid
  - FPHam/L3-8B-Everything-COT
  - FPHam/L3-8B-Everything-COT
  - FPHam/L3-8B-Everything-COT
tags:
  - merge
  - mergekit
  - lazymergekit
  - SvalTek/L3-ColdBrew-Astrid
  - FPHam/L3-8B-Everything-COT

L3-ColdBrew-Arcadia

L3-ColdBrew-Arcadia is a merge of the following models using LazyMergekit:

🧩 Configuration

merge_method: passthrough
slices:
  # Lower Layers (0–11): ColdBrew’s foundation
  - sources:
      - layer_range: [0, 12]
        model: SvalTek/L3-ColdBrew-Astrid

  # Reasoning Layers (12–23): Use FPHam for logical depth
  - sources:
      - layer_range: [12, 24]
        model: FPHam/L3-8B-Everything-COT

  # Reflection Layers (24–31): Use FPHam for reasoning and reflection
  - sources:
      - layer_range: [24, 32]
        model: FPHam/L3-8B-Everything-COT

  # Duplicate Layers (24–31): Add valid parameter growth
  - sources:
      - layer_range: [24, 32]  # First duplicate
        model: FPHam/L3-8B-Everything-COT
      - layer_range: [24, 32]  # Second duplicate
        model: FPHam/L3-8B-Everything-COT

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "SvalTek/L3-ColdBrew-Arcadia"
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"])