Spaces:
Running
on
Zero
Running
on
Zero
import spaces | |
import torch | |
import gradio as gr | |
from huggingface_hub import snapshot_download | |
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
model = None | |
model_id = "nazimali/Mistral-Nemo-Kurdish-Instruct" | |
infer_prompt = """Li jêr rêwerzek heye ku peywirek rave dike, bi têketinek ku çarçoveyek din peyda dike ve tê hev kirin. Bersivek ku daxwazê bi guncan temam dike binivîsin. | |
### Telîmat: | |
{} | |
### Têketin: | |
{} | |
### Bersiv: | |
""" | |
snapshot_download("nazimali/Mistral-Nemo-Kurdish") | |
snapshot_download(repo_id=model_id, ignore_patterns=["*.gguf"]) | |
def respond( | |
message, | |
history: list[tuple[str, str]], | |
): | |
global model, tokenizer | |
if model is None: | |
bnb_config = BitsAndBytesConfig( | |
load_in_4bit=True, | |
bnb_4bit_use_double_quant=True, | |
bnb_4bit_quant_type="nf4", | |
bnb_4bit_compute_dtype=torch.bfloat16, | |
) | |
model = AutoModelForCausalLM.from_pretrained( | |
model_id, | |
quantization_config=bnb_config, | |
device_map="auto", | |
) | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
model.eval() | |
prompt = infer_prompt.format("tu arîkarek alîkar î", message) | |
input_ids = tokenizer( | |
prompt, | |
return_tensors="pt", | |
add_special_tokens=False, | |
return_token_type_ids=False, | |
).to("cuda") | |
with torch.inference_mode(): | |
generated_ids = model.generate( | |
**input_ids, | |
max_new_tokens=120, | |
do_sample=True, | |
temperature=0.7, | |
top_p=0.7, | |
num_return_sequences=1, | |
pad_token_id=tokenizer.pad_token_id, | |
eos_token_id=tokenizer.eos_token_id, | |
) | |
decoded_output = tokenizer.batch_decode(generated_ids)[0] | |
return decoded_output.replace(prompt, "").replace("</s>", "") | |
demo = gr.ChatInterface(respond, type="messages", examples=["سڵاو ئەلیکوم، چۆنیت؟", "Selam alikum, tu çawa yî?", "Peace be upon you, how are you?"], title="Mistral Nemo Kurdish Instruct") | |
if __name__ == "__main__": | |
demo.launch() |