feat: qwen demo code

Season2.step_into_llm/17.Qwen/CLI_input_mock_qwen.py

@@ -0,0 +1,48 @@
+import mindspore
+import numpy as np
+from mindspore import dtype as mstype
+import mindspore.ops as ops
+from mindspore import Tensor
+from mindnlp.transformers import AutoTokenizer, AutoModelForCausalLM
+import faulthandler
+
+faulthandler.enable()
+
+model_id = "Qwen/Qwen1.5-0.5B-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_id, mirror='modelscope')
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    ms_dtype=mindspore.float16,
+    mirror='modelscope'
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="ms"
+)
+attention_mask = Tensor(np.ones(input_ids.shape), mstype.float32)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|endoftext|>")
+]
+outputs = model.generate(
+    input_ids,
+    attention_mask=attention_mask,
+    max_new_tokens=20,
+    eos_token_id=terminators,
+    do_sample=False,
+    # do_sample=True,
+    # temperature=0.6,
+    # top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(outputs)
+print(tokenizer.decode(response, skip_special_tokens=True))
+

Season2.step_into_llm/17.Qwen/GUI_gradio-qwen1.5.py

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+import gradio as gr
+import mindspore
+from mindspore import dtype as mstype
+import numpy as np
+from mindnlpv041.mindnlp.transformers import AutoModelForCausalLM, AutoTokenizer
+from mindnlpv041.mindnlp.transformers import TextIteratorStreamer
+from threading import Thread
+
+# Loading the tokenizer and model from Hugging Face's model hub.
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat", ms_dtype=mindspore.float16)
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B-Chat", ms_dtype=mindspore.float16)
+
+system_prompt = "You are a helpful and friendly chatbot"
+
+def build_input_from_chat_history(chat_history, msg: str):
+    messages = [{'role': 'system', 'content': system_prompt}]
+    for user_msg, ai_msg in chat_history:
+        messages.append({'role': 'user', 'content': user_msg})
+        messages.append({'role': 'assistant', 'content': ai_msg})
+    messages.append({'role': 'user', 'content': msg})
+    return messages
+
+# Function to generate model predictions.
+def predict(message, history):
+    # Formatting the input for the model.
+    messages = build_input_from_chat_history(history, message)
+    input_ids = tokenizer.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            return_tensors="ms",
+            tokenize=True
+        )
+    attention_mask = mindspore.Tensor(np.ones(input_ids.shape), mstype.float32)
+    streamer = TextIteratorStreamer(tokenizer, timeout=300, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.9,
+        temperature=0.1,
+        num_beams=1,
+        attention_mask=attention_mask,
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()  # Starting the generation in a separate thread.
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if '</s>' in partial_message:  # Breaking the loop if the stop token is generated.
+            break
+        yield partial_message
+
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 title="Qwen1.5-0.5b-Chat",
+                 description="问几个问题",
+                 examples=['你是谁？', '介绍一下华为公司']
+                 ).launch(share=True, server_name='0.0.0.0', server_port=7860)  # Launching the web interface.
+