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1models:
2 - model: mistralai/Mistral-7B-Instruct-v0.2
3 # No parameters necessary for base model
4 - model: mlabonne/NeuralBeagle14-7B
5 parameters:
6 density: 0.53
7 weight: 0.3
8 - model: timpal0l/Mistral-7B-v0.1-flashback-v2
9 parameters:
10 density: 0.53
11 weight: 0.3
12 - model: Nexusflow/Starling-LM-7B-beta
13 parameters:
14 density: 0.53
15 weight: 0.2
16 - model: neph1/bellman-7b-mistral-instruct-v0.2
17 parameters:
18 density: 0.53
19 weight: 0.2
20merge_method: dare_ties
21base_model: mistralai/Mistral-7B-Instruct-v0.2
22parameters:
23 int8_mask: true
24dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "Knobi3/SwedishBellmanBeagle-dareties"
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"])