metadata

base_model:
  - Sao10K/L3-8B-Stheno-v3.2
  - Sao10K/L3-8B-Stheno-v3.2
  - Sao10K/L3-8B-Stheno-v3.1
  - Sao10K/L3-8B-Stheno-v3.1
tags:
  - merge
  - mergekit
  - lazymergekit
  - Sao10K/L3-8B-Stheno-v3.2
  - Sao10K/L3-8B-Stheno-v3.1
  - not-for-all-audiences
license: apache-2.0

L3-15B-Stheno-passthrough

L3-15B-Stheno-passthrough is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
- sources:
  - layer_range: [0, 24]
    model: Sao10K/L3-8B-Stheno-v3.2
- sources:
  - layer_range: [8, 24]
    model: Sao10K/L3-8B-Stheno-v3.2
    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-Stheno-v3.1
    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-Stheno-v3.1
dtype: bfloat16
merge_method: passthrough

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/L3-15B-Stheno-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"])