Upload 3 files

README.md

@@ -1,12 +1,13 @@
----
-title: Test Qwen Multi
-emoji: 📊
-colorFrom: yellow
-colorTo: pink
-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 multi
+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

@@ -0,0 +1,64 @@
+import os
+import spaces
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import HfApi
+import time
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+MODELS = ["Qwen/Qwen2.5-Coder-0.5B", "Qwen/Qwen2.5-Coder-1.5B"]
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = MODELS[0]
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def load_model(repo_id: str, progress = gr.Progress(track_tqdm=True)):
+    global model, tokenizer
+    api = HfApi(token=HF_TOKEN)
+    if not api.repo_exists(repo_id=repo_id, token=HF_TOKEN): raise gr.Error(f"Model not found: {repo_id}")
+    model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto", device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(repo_id)
+    gr.Info(f"Model loaded {repo_id}")
+    return repo_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)
+    start = time.time()
+    generated_ids = model.generate(**inputs, max_new_tokens=tokens)
+    end = time.time()
+    elapsed_sec = end - start
+    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(f"Input: {message}")
+    print(f"Output: {output_str}")
+    print(f"Elapsed time: {elapsed_sec} sec.")
+
+    output_md = f"### {output_str}"
+    info_md = f"### Elapsed time: {elapsed_sec} sec."
+
+    return output_md, info_md
+
+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)
+        model_name = gr.Dropdown(label="Model", choices=MODELS, value=MODELS[0], allow_custom_value=True)
+        #image_url = gr.Textbox(label="Image URL", value=url, lines=1)
+    run_button = gr.Button("Run", variant="primary")
+    output_md = gr.Markdown("<br><br>")
+    info_md = gr.Markdown("<br><br>")
+
+    run_button.click(infer, [message, sysprompt, tokens], [output_md, info_md])
+    model_name.change(load_model, [model_name], [model_name])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+huggingface_hub>=0.26.1
+torch
+transformers>=4.45.0
+bitsandbytes
+accelerate>=1.0.1
+numpy<2
+datasets>3.0.2