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1models:
2 - model: FelixChao/WestSeverus-7B-DPO-v2
3 parameters:
4 density: [1, 0.7, 0.1]
5 weight: [0, 0.3, 0.7, 1]
6 - model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
7 parameters:
8 density: [1, 0.7, 0.3]
9 weight: [0, 0.25, 0.5, 1]
10 - model: mlabonne/Daredevil-7B
11 parameters:
12 density: 0.33
13 weight:
14 - filter: mlp
15 value: [0.35, 0.65]
16 - value: 0
17merge_method: ties
18base_model: mistralai/Mistral-7B-v0.1
19parameters:
20 int8_mask: true
21 normalize: true
22 t:
23 - filter: lm_head
24 value: [0.55]
25 - filter: embed_tokens
26 value: [0.7]
27 - filter: self_attn
28 value: [0.65, 0.35]
29 - filter: mlp
30 value: [0.35, 0.65]
31 - filter: layernorm
32 value: [0.4, 0.6]
33 - filter: modelnorm
34 value: [0.6]
35 - value: 0.5 # fallback for rest of tensors
36dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "jsfs11/WONMSeverusDevilv2-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"])