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import os |
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from collections.abc import Iterator |
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from threading import Thread |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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DESCRIPTION = """\ |
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# SILMA Kashif 2B Instruct v1.0 Playground |
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This is a demo of [`silma-ai/SILMA-Kashif-2B-Instruct-v1.0`](https://huggingface.co/silma-ai/SILMA-Kashif-2B-Instruct-v1.0). |
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** NOTE: Kashif is a RAG model, it is only trained to answer questions based on context. |
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""" |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "10096")) |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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model_id = "silma-ai/SILMA-Kashif-2B-Instruct-v1.0" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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) |
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model.config.sliding_window = 4096 |
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model.eval() |
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@spaces.GPU(duration=90) |
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def generate( |
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message: str, |
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chat_history: list[dict], |
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max_new_tokens: int = 1024, |
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temperature: float = 0.01, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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repetition_penalty: float = 1.2, |
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) -> Iterator[str]: |
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conversation = chat_history.copy() |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt") |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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repetition_penalty=repetition_penalty, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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demo = gr.ChatInterface( |
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fn=generate, |
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additional_inputs=[ |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=4.0, |
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step=0.1, |
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value=0.6, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1.2, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["Hello there! How are you doing?"], |
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["Can you explain briefly to me what is the Python programming language?"], |
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], |
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cache_examples=False, |
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type="messages", |
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description=DESCRIPTION, |
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css_paths="style.css", |
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fill_height=True, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
