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1models:
2 - model: kaist-ai/mistral-orpo-beta
3 parameters:
4 density: 0.5
5 weight: 0.6
6 # No parameters necessary for base model
7 - model: mlabonne/NeuralBeagle14-7B
8 parameters:
9 density: 0.5
10 weight: 0.4
11merge_method: dare_ties
12base_model: kaist-ai/mistral-orpo-beta
13parameters:
14 int8_mask: true
15dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties"
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"])