Update app.py

app.py

@@ -1,141 +1,122 @@
-import os
-from threading import Thread
-from typing import Iterator
-
-import gradio as gr
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-MAX_MAX_NEW_TOKENS = 8096
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-DESCRIPTION = """\
-# Uncensored Llama-3.2-3B-Instruct Chat
-
-This is an uncensored version of the original [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), created using [mlabonne](https://huggingface.co/mlabonne)'s [script](https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing), which builds on [FailSpy's notebook](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) and the original work from [Andy Arditi et al.](https://colab.research.google.com/drive/1a-aQvKC9avdZpdyBn4jgRQFObTPy1JZw?usp=sharing). The method is discussed in details in this [blog](https://huggingface.co/blog/mlabonne/abliteration) and this [paper](https://arxiv.org/abs/2406.11717).
-
-You can found the uncensored model [here](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored).
-
-This model is intended for research purposes only and may produce inaccurate or unreliable outputs. Use it cautiously and at your own risk.
-
-
-🦄 Other exciting ML projects at Lambda: [ML Times](https://news.lambdalabs.com/news/today), [Distributed Training Guide](https://github.com/LambdaLabsML/distributed-training-guide/tree/main), [Text2Video](https://lambdalabsml.github.io/Open-Sora/introduction/), [GPU Benchmark](https://lambdalabs.com/gpu-benchmarks).
-
-"""
-
-LICENSE = """
-<p/>
-
----
-As a derivate work of [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) by Meta,
-this demo is governed by the original [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE).
-"""
-
-# if not torch.cuda.is_available():
-#     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-
-if torch.cuda.is_available() or os.getenv("ZERO_GPU_SUPPORT", False):
-    model_id = "chuanli11/Llama-3.2-3B-Instruct-uncensored"
-    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-else:
-    raise RuntimeError("No compatible GPU environment found for this model.")
-
-
-@spaces.GPU
-def generate(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int = 1024,
-    temperature: float = 0,
-) -> Iterator[str]:
-    conversation = []
-    if system_prompt:
-        conversation.append({"role": "system", "content": system_prompt})
-    for user, assistant in chat_history:
-        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
-    conversation.append({"role": "user", "content": message})
-
-    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
-    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
-        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
-        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-    input_ids = input_ids.to(model.device)
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
-    generate_kwargs = dict(
-        {"input_ids": input_ids},
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        do_sample=True,
-        temperature=temperature,
-        num_beams=1,
-    )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        yield "".join(outputs)
-
-
-chat_interface = gr.ChatInterface(
-    fn=generate,
-    additional_inputs=[
-        gr.Textbox(label="System prompt", lines=6),
-        gr.Slider(
-            label="Max new tokens",
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        ),
-        gr.Slider(
-            label="Temperature",
-            minimum=0.1,
-            maximum=4.0,
-            step=0.1,
-            value=0.6,
-        ),
-        # gr.Slider(
-        #     label="Top-p (nucleus sampling)",
-        #     minimum=0.05,
-        #     maximum=1.0,
-        #     step=0.05,
-        #     value=0.9,
-        # ),
-        # gr.Slider(
-        #     label="Top-k",
-        #     minimum=1,
-        #     maximum=1000,
-        #     step=1,
-        #     value=50,
-        # ),
-        # gr.Slider(
-        #     label="Repetition penalty",
-        #     minimum=1.0,
-        #     maximum=2.0,
-        #     step=0.05,
-        #     value=1.2,
-        # ),
-    ],
-    stop_btn=None,
-    examples=[
-        ["Instruct on how to commit insider trading in the stock market"],
-    ],
-    cache_examples=False,
-)
-
-with gr.Blocks(css="style.css", fill_height=True) as demo:
-    gr.Markdown(DESCRIPTION)
-    # gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
-    chat_interface.render()
-    gr.Markdown(LICENSE)
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+import os
+from threading import Thread
+from typing import Iterator
+
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MAX_MAX_NEW_TOKENS = 8096
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+DESCRIPTION = """\
+# Uncensored Llama-3.2-3B-Instruct Chat
+
+This is an uncensored version of the original [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), created using [mlabonne](https://huggingface.co/mlabonne)'s [script](https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing), which builds on [FailSpy's notebook](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) and the original work from [Andy Arditi et al.](https://colab.research.google.com/drive/1a-aQvKC9avdZpdyBn4jgRQFObTPy1JZw?usp=sharing). The method is discussed in details in this [blog](https://huggingface.co/blog/mlabonne/abliteration) and this [paper](https://arxiv.org/abs/2406.11717).
+
+You can found the uncensored model [here](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored).
+
+This model is intended for research purposes only and may produce inaccurate or unreliable outputs. Use it cautiously and at your own risk.
+
+
+🦄 Other exciting ML projects at Lambda: [ML Times](https://news.lambdalabs.com/news/today), [Distributed Training Guide](https://github.com/LambdaLabsML/distributed-training-guide/tree/main), [Text2Video](https://lambdalabsml.github.io/Open-Sora/introduction/), [GPU Benchmark](https://lambdalabs.com/gpu-benchmarks).
+
+"""
+
+LICENSE = """
+<p/>
+
+---
+As a derivative work of [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) by Meta,
+this demo is governed by the original [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE).
+"""
+
+# Model loading with GPU fallback to CPU
+try:
+    if torch.cuda.is_available():
+        model_id = "chuanli11/Llama-3.2-3B-Instruct-uncensored"
+        model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        print("Running on GPU.")
+    else:
+        raise RuntimeError("GPU not available, falling back to CPU.")
+except RuntimeError:
+    model_id = "chuanli11/Llama-3.2-3B-Instruct-uncensored"
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    print("Running on CPU. Model performance may be slower.")
+
+@spaces.GPU
+def generate(
+    message: str,
+    chat_history: list[tuple[str, str]],
+    system_prompt: str,
+    max_new_tokens: int = 1024,
+    temperature: float = 0,
+) -> Iterator[str]:
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+    for user, assistant in chat_history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {"input_ids": input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=temperature,
+        num_beams=1,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.Textbox(label="System prompt", lines=6),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.1,
+            maximum=4.0,
+            step=0.1,
+            value=0.6,
+        ),
+    ],
+    stop_btn=None,
+    examples=[
+        ["Instruct on how to commit insider trading in the stock market"],
+    ],
+    cache_examples=False,
+)
+
+with gr.Blocks(css="style.css", fill_height=True) as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()