DimasMP3 commited on
Commit ·
cb9e216
1
Parent(s): cc1cfc8
refactor: Configure model for CPU-only execution by removing 4-bit quantization, setting float32 dtype, and updating UI descriptions and generation parameters.
Browse files
app.py
CHANGED
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@@ -1,26 +1,17 @@
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from threading import Thread
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MODEL_ID = "DimasMP3/qwen2.5-math-finetuned-7b"
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16
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)
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print(f"System: Loading model {MODEL_ID}...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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# 2. Load Model dengan Config Baru
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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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low_cpu_mem_usage=True
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)
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@@ -38,19 +29,19 @@ Solve the following math problem step-by-step:
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def predict(message, history):
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prompt = format_prompt(message)
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inputs = tokenizer([prompt], return_tensors="pt")
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streamer = TextIteratorStreamer(
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tokenizer,
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skip_prompt=True,
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skip_special_tokens=True,
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timeout=
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)
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generation_kwargs = dict(
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inputs,
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streamer=streamer,
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max_new_tokens=
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do_sample=True,
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temperature=0.3,
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top_p=0.9,
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@@ -67,12 +58,10 @@ def predict(message, history):
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demo = gr.ChatInterface(
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fn=predict,
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title="
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description="Qwen 2.5 (7B
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examples=[
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"Solve
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"Calculate the derivative of f(x) = 4x^3 - 2x",
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"A triangle has a base of 10cm and a height of 5cm, what is its area?"
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],
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cache_examples=False,
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)
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from threading import Thread
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MODEL_ID = "DimasMP3/qwen2.5-math-finetuned-7b"
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print(f"System: Loading model {MODEL_ID} on CPU...")
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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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torch_dtype=torch.float32,
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low_cpu_mem_usage=True
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)
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def predict(message, history):
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prompt = format_prompt(message)
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inputs = tokenizer([prompt], return_tensors="pt")
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streamer = TextIteratorStreamer(
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tokenizer,
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skip_prompt=True,
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skip_special_tokens=True,
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timeout=60.0
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)
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generation_kwargs = dict(
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inputs,
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streamer=streamer,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.3,
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top_p=0.9,
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demo = gr.ChatInterface(
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fn=predict,
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title="Sultan Math AI Solver (CPU Mode)",
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description="Qwen 2.5 (7B) running on CPU. Might be slow!",
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examples=[
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"Solve 3x + 10 = 25",
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],
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cache_examples=False,
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)
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