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1models:
2 - model: liminerity/M7-7b
3 # no parameters necessary for base model
4 - model: liminerity/M7-7b
5 parameters:
6 weight: 0.2
7 density: 0.88
8 - model: Kukedlc/NeuralSirKrishna-7b
9 parameters:
10 weight: 0.1
11 density: 0.66
12 - model: Kukedlc/MyModelsMerge-7b
13 parameters:
14 weight: 0.1
15 density: 0.66
16 - model: AurelPx/Percival_01-7b-slerp
17 parameters:
18 weight: 0.1
19 density: 0.33
20 - model: MatthieuJ/Jason1903_SLERP
21 parameters:
22 weight: 0.1
23 density: 0.33
24 - model: MTSAIR/multi_verse_model
25 parameters:
26 weight: 0.1
27 density: 0.66
28 - model: Gille/StrangeMerges_30-7B-slerp
29 parameters:
30 weight: 0.1
31 density: 0.55
32 - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
33 parameters:
34 weight: 0.1
35 density: 0.22
36 - model: yam-peleg/Experiment28-7B
37 parameters:
38 weight: 0.1
39 density: 0.44
40 - model: mlabonne/UltraMerge-7B
41 parameters:
42 weight: 0.1
43 density: 0.77
44merge_method: dare_ties
45base_model: liminerity/M7-7b
46
47parameters:
48 int8_mask: true
49 normalize: true
50dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Kukedlc/SomeModelsMerge-7b"
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"])