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1models:
2 - model: TheDrummer/Fallen-Gemma3-12B-v1
3 #no parameters necessary for base model
4 - model: IlyaGusev/saiga_gemma3_12b
5 parameters:
6 density: 0.5
7 weight: 0.8
8
9 - model: zelk12/MT1-gemma-3-12B
10 parameters:
11 density: 0.507
12 weight: 0.792
13
14 - model: soob3123/amoral-gemma3-12B-v2
15 parameters:
16 density: 0.615
17 weight: 0.684
18
19 - model: zelk12/MT-Gen1-gemma-3-12B
20 parameters:
21 density: 0.781
22 weight: 0.518
23
24 - model: zelk12/MT-gemma-3-12B
25 parameters:
26 density: 0.8
27 weight: 0.5
28
29merge_method: dare_ties
30base_model: TheDrummer/Fallen-Gemma3-12B-v1
31parameters:
32 normalize: true
33dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "zelk12/MT6-Gen3_gemma-3-12B"
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"])