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1slices:
2models:
3 - model: NousResearch/Meta-Llama-3-8B
4 # No parameters necessary for base model
5 - model: catrinbaze/llama-refueled-merge
6 parameters:
7 density: 0.6
8 weight: 0.6
9 - model: NousResearch/Meta-Llama-3-8B-instruct
10 parameters:
11 density: 0.58
12 weight: 0.2
13 - model: Locutusque/Llama-3-Orca-1.0-8B
14 parameters:
15 density: 0.56
16 weight: 0.05
17 - model: lighteternal/Llama3-merge-biomed-8b
18 parameters:
19 density: 0.56
20 weight: 0.05
21 - model: mlabonne/NeuralDaredevil-8B-abliterated
22 parameters:
23 density: 0.55
24 weight: 0.05
25 - model: mlabonne/Daredevil-8B
26 parameters:
27 density: 0.55
28 weight: 0.05
29merge_method: dare_ties
30base_model: NousResearch/Meta-Llama-3-8B
31dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "catrinbaze/merge-llama-3-8b"
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"])