update

README.md

@@ -1,8 +1,8 @@
 ---
-title: Qwen2 05b
-emoji: 🦀
-colorFrom: blue
-colorTo: red
+title: qwen2-05b
+emoji: 🐠
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
 sdk_version: 4.40.0
 app_file: app.py

app.py

@@ -0,0 +1,71 @@
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model_name = "Qwen/Qwen1.5-0.5B-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def generate_text(text):
+    # Tokenize the input text, including attention mas
+    #input_ids = tokenizer(text, return_tensors="pt", padding=True)
+
+
+
+    messages = []
+    use_system_prompt = True
+    DEFAULT_SYSTEM_PROMPT = "you are helpfull assistant."
+    if use_system_prompt:
+        messages = [
+                {"role": "system", "content": DEFAULT_SYSTEM_PROMPT}
+            ]
+
+        user_messages = [
+            {"role": "user", "content": text}
+        ]
+        messages += user_messages
+
+    prompt = tokenizer.apply_chat_template(
+            conversation=messages,
+            add_generation_prompt=True,
+            tokenize=False
+        )
+    
+    input_datas = tokenizer(
+        prompt,
+        add_special_tokens=True,
+        return_tensors="pt"
+        )
+
+    # Generate text, passing the attention mask
+    generated_ids = model.generate(input_ids=input_datas.input_ids, attention_mask=input_datas.attention_mask,max_length=10000)
+    #generated_ids = model.generate(input_ids=input_ids, max_length=100)
+    
+    # Decode the generated tokens
+    generated_text = tokenizer.decode(generated_ids[0][input_datas.input_ids.size(1) :], skip_special_tokens=True)
+
+    # Print the generated text
+    #print(generated_text)
+    return generated_text
+
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+#app.logger.disabled = True
+#log = logging.getLogger('werkzeug')
+#log.disabled = True
+
+@app.route('/')
+def predict():
+    param_value = request.args.get('param', '') 
+    # ここにモデルの推論ロジックを追加
+    #output = pipe(messages, **generation_args) 
+    #text = (output[0]['generated_text'])
+    #print("hello")
+    #result = {"prediction": "dummy_result"}
+    text = generate_text(param_value)
+    return f"{text}"
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

requirements.txt

@@ -0,0 +1,5 @@
+llama-cpp-python
+transformers
+torch
+accelerate
+flask