Instructions to use blackmount8/falcon-7b-instruct-ct2-int8_float16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blackmount8/falcon-7b-instruct-ct2-int8_float16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blackmount8/falcon-7b-instruct-ct2-int8_float16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Sample Code for int8_float16
#1
by lazyDataScientist - opened
I haven't tested this but this should help handle the int8_float16
import ctranslate2
from transformers import AutoTokenizer
model_name = "blackmount8/falcon-7b-instruct-ct2-int8_float16"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left")
model = ctranslate2.Generator(model_name, device="auto", compute_type="float16")
input_text = ["What is the meaning of stonehenge?", "Hello mate!"]
input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids
input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids]
outputs = model.generate_batch(input_tokens, max_length=128)
output_tokens = [
ele.sequences_ids[0] for ele in outputs
]
output = tokenizer.batch_decode(output_tokens)
print(output)