README.md

metadata

base_model:
  - Sao10K/L3-8B-Lunaris-v1
  - Sao10K/L3-8B-Lunaris-v1
  - Sao10K/L3-8B-Lunaris-v1
  - Sao10K/L3-8B-Lunaris-v1
tags:
  - merge
  - mergekit
  - lazymergekit
  - Sao10K/L3-8B-Lunaris-v1

L3-Lunaris-v1-15B

L3-Lunaris-v1-15B is a merge of the following models using LazyMergekit:

• Sao10K/L3-8B-Lunaris-v1
• Sao10K/L3-8B-Lunaris-v1
• Sao10K/L3-8B-Lunaris-v1
• Sao10K/L3-8B-Lunaris-v1

🧩 Configuration

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 24]
    model: Sao10K/L3-8B-Lunaris-v1
- sources:
  - layer_range: [8, 24]
    model: Sao10K/L3-8B-Lunaris-v1
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [8, 24]
    model: Sao10K/L3-8B-Lunaris-v1
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [24, 32]
    model: Sao10K/L3-8B-Lunaris-v1

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Tremontaine/L3-Lunaris-v1-15B"
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"])