metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - SuperAGI/SAM
  - GoogleAI/Gemini
  - bigscience/bloom
  - openai/opt-175b
  - deepmind/gopher
  - microsoft/megatron-turing-nlg
base_model:
  - SuperAGI/SAM
  - GoogleAI/Gemini
  - bigscience/bloom
  - openai/opt-175b
  - deepmind/gopher
  - microsoft/megatron-turing-nlg

SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp

SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: SuperAGI/SAM
        layer_range: [0, 32]
      - model: GoogleAI/Gemini
        layer_range: [0, 32]
      - model: bigscience/bloom
        layer_range: [0, 32]
      - model: openai/opt-175b
        layer_range: [0, 32]
      - model: deepmind/gopher
        layer_range: [0, 32]
      - model: microsoft/megatron-turing-nlg
        layer_range: [0, 32]
merge_method: slerp
base_model: SuperAGI/SAM
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat1

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp"
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"])