Running
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The SmolTuners group is a community dedicated to the development of small-scale Large Language Models (LLMs) using consumer-grade GPUs.
import chromadb
from your_custom_fn import CustomEmbeddingFunction
class ChromaStorage:
def __init__(self, config):
self.config = config
self.client = self.init_client()
self.embedding_function = CustomEmbeddingFunction()
def check_config(self):
assert os.path.exists(self.config.path), ValueError('Provided path does not exists!!')
def init_client(self):
return chromadb.PersistentClient(path = self.config.path,)
def init_collection(self, name: str):
return self.client.get_or_create_collection(name = name, embedding_function = self.embedding_function)
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'
axolotl\examples\llama-2\qlora.yml
base_model: NousResearch/Llama-2-7b-hf
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml
Lets become a mad scientist, download all possible data and train model for months to get the proper one.
ct2-transformers-converter --model Helsinki-NLP/opus-mt-pl-en --output_dir opus-mt-pl-en