Kex
Collection
Models and datasets created as part of Bachelor thesis
β’
16 items
β’
Updated
MisTyr-ties is a merge of the following models using LazyMergekit:
models:
- model: AI-Sweden-Models/tyr
# no parameters necessary for base model
- model: timpal0l/Mistral-7B-v0.1-flashback-v2
parameters:
density: 0.5
weight: 0.5
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
parameters:
density: 0.5
weight: 0.3
- model: RJuro/munin-neuralbeagle-7b
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
merge_method: ties
base_model: AI-Sweden-Models/tyr
parameters:
normalize: true
dtype: bfloat16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "FredrikBL/MisTyr-ties"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])