upload files

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+)
+import os
+from threading import Thread
+import spaces
+import time
+import subprocess
+
+subprocess.run(
+    "pip install flash-attn --no-build-isolation",
+    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
+    shell=True,
+)
+
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-small-128k-instruct",
+    torch_dtype="auto", 
+    trust_remote_code=True,
+)
+tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-small-128k-instruct",trust_remote_code=True,)
+terminators = [
+    tok.eos_token_id,
+]
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
+else:
+    device = torch.device("cpu")
+    print("Using CPU")
+
+model = model.to(device)
+# Dispatch Errors
+
+
+@spaces.GPU(duration=60)
+def chat(message, history,system_prompt, temperature, do_sample, max_tokens, top_k, repetition_penalty, top_p):
+    chat = [
+        {"role": "assistant", "content": system_prompt}
+    ]
+    for item in history:
+        chat.append({"role": "user", "content": item[0]})
+        if item[1] is not None:
+            chat.append({"role": "assistant", "content": item[1]})
+    chat.append({"role": "user", "content": message})
+    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    model_inputs = tok([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(
+        tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True
+    )
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        eos_token_id=terminators,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        top_p=top_p
+    )
+
+    if temperature == 0:
+        generate_kwargs["do_sample"] = False
+
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+    yield partial_text
+
+
+demo = gr.ChatInterface(
+    fn=chat,
+    examples=[["Write me a poem about Machine Learning."],
+              ["write fibonacci sequence in python"],
+              ["who won the world cup in 2018?"],
+              ["when was the first computer invented?"],
+              ],
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
+    additional_inputs=[
+        gr.Textbox("Perform the task to the best of your ability.", label="System prompt"),
+        gr.Slider(
+            minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
+        ),
+        gr.Checkbox(label="Sampling", value=True),
+        gr.Slider(
+            minimum=128,
+            maximum=4096,
+            step=1,
+            value=512,
+            label="Max new tokens",
+            render=False,
+        ),
+        gr.Slider(1, 80, 40, label="Top K sampling"),
+        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
+        gr.Slider(0, 1, 0.95, label="Top P sampling"),
+    ],
+    stop_btn="Stop Generation",
+    title="Chat With Phi-3-small-128k-instruct",
+    description="[microsoft/Phi-3-small-128k-instruct](https://huggingface.co/microsoft/Phi-3-small-128k-instruct)",
+)
+demo.launch()

requirements.txt

@@ -0,0 +1,9 @@
+tiktoken
+gradio
+spaces
+torch==2.2.0
+git+https://github.com/huggingface/transformers/
+optimum
+accelerate
+bitsandbytes
+pytest