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1slices:
2 - sources:
3 - model: SuperAGI/SAM
4 layer_range: [0, 32]
5 - model: GoogleAI/Gemini
6 layer_range: [0, 32]
7 - model: bigscience/bloom
8 layer_range: [0, 32]
9 - model: openai/opt-175b
10 layer_range: [0, 32]
11 - model: deepmind/gopher
12 layer_range: [0, 32]
13 - model: microsoft/megatron-turing-nlg
14 layer_range: [0, 32]
15merge_method: slerp
16base_model: SuperAGI/SAM
17parameters:
18 t:
19 - filter: self_attn
20 value: [0, 0.5, 0.3, 0.7, 1]
21 - filter: mlp
22 value: [1, 0.5, 0.7, 0.3, 0]
23 - value: 0.5
24dtype: bfloat11!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])