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1slices:
2 - sources:
3 - model: Open-Orca/Mistral-7B-OpenOrca
4 layer_range: [0, 32]
5 - model: Crystalcareai/Evol-Mistral
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: Open-Orca/Mistral-7B-OpenOrca
9parameters:
10 t:
11 - filter: self_attn
12 value: [0, 0.5, 0.3, 0.7, 1]
13 - filter: mlp
14 value: [1, 0.5, 0.7, 0.3, 0]
15 - value: 0.5
16dtype: bfloat16
17experts:
18 - source_model: Open-Orca/Mistral-7B-OpenOrca
19 positive_prompts:
20 - "chat"
21 - "reasoning"
22 - "Why would"
23 - "explain"
24 - source_model: Crystalcareai/Evol-Mistral
25 positive_prompts:
26 - "instruction"
27 - "create a"
28 - "You must"
29 - "Your job"1!pip install -qU transformers bitsandbytes accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Crystalcareai/Evolorxa-14b"
8
9tokenizer = AutoTokenizer.from_pretrained(model)
10pipeline = transformers.pipeline(
11 "text-generation",
12 model=model,
13 model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
14)
15
16messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
17prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
18outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
19print(outputs[0]["generated_text"])