transformers

app.py

@@ -1,51 +1,29 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import pipeline
+
 import os
 HF_TOKEN = os.getenv('HF_TOKEN')
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("zidsi/SLlamica_PT4SFT_v2",token=HF_TOKEN)
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
+checkpoint = "zidsi/SLlamica_PT4SFT_v2"
+device = "cuda"  # "cuda" or "cpu"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint,token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(checkpoint,token=HF_TOKEN).to(device)
 
+def predict(message, history):
+    history.append({"role": "user", "content": message})
+    input_text = tokenizer.apply_chat_template(history, tokenize=False)
+    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)  
+    outputs = model.generate(inputs, max_new_tokens=100, temperature=0.2, top_p=0.9, do_sample=True)
+    decoded = tokenizer.decode(outputs[0])
+    response = decoded.split("<|im_start|>assistant\n")[-1].split("<|im_end|>")[0]
+    return response
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 demo = gr.ChatInterface(
-    respond,
+    predict, type="messages",
     additional_inputs=[
         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),