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1models:
2 - model: BioMistral/BioMistral-7B-DARE
3 # No parameters necessary for base model
4 - model: Weyaxi/MetaMath-OpenHermes-2.5-neural-chat-7b-v3-1-7B-Linear
5 parameters:
6 density: 0.5
7 weight: 0.2
8
9
10 - model: icefog72/IceMoonshineRP-7b
11 parameters:
12 density: 0.5
13 weight: 0.2
14
15
16 - model: Weyaxi/MetaMath-neural-chat-7b-v3-2-Slerp
17 parameters:
18 density: 0.5
19 weight: 0.2
20
21
22 - model: VAGOsolutions/SauerkrautLM-7b-HerO
23 parameters:
24 density: 0.5
25 weight: 0.2
26
27
28 - model: mrfakename/NeuralOrca-7B-v1
29 parameters:
30 density: 0.5
31 weight: 0.2
32
33
34merge_method: dare_ties
35base_model: BioMistral/BioMistral-7B-DARE
36parameters:
37 int8_mask: true
38dtype: bfloat16
391!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "kainatq/kangaroo_7B_test01"
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"])