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README.md

@@ -1,12 +1,13 @@
----
-title: Test Qwen2.5 Coder 1.5B Instruct
-emoji: 🐠
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.6.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: test Qwen2.5-Coder-1.5B-Instruct
+emoji: 🙄
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import spaces
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+@spaces.GPU(duration=30)
+def infer(message: str, sysprompt: str, tokens: int=30):
+    messages = [
+        {"role": "system", "content": sysprompt},
+        {"role": "user", "content": message}
+    ]
+    
+    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(text=[input_text], return_tensors="pt").to(model.device)
+    generated_ids = model.generate(**inputs, max_new_tokens=tokens)
+    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)]
+    output_str = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    print(message)
+    print(output_str)
+
+    return output_str
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        message = gr.Textbox(label="Message", value="", lines=1)
+        sysprompt = gr.Textbox(label="System prompt", value="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.", lines=4)
+        tokens = gr.Slider(label="Max tokens", value=30, minimum=1, maximum=2048, step=1)
+        #image_url = gr.Textbox(label="Image URL", value=url, lines=1)
+    run_button = gr.Button("Run", variant="primary")
+    info_md = gr.Markdown("<br><br><br>")
+
+    run_button.click(infer, [message, sysprompt, tokens], [info_md])
+
+demo.launch()

requirements.txt

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+huggingface_hub>=0.26.1
+torch
+transformers>=4.45.0
+bitsandbytes
+accelerate>=1.0.1
+numpy<2
+datasets>3.0.2