Add application file

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+from threading import Thread
+
+MODEL = "yava-code/kimi-coder-135m"  
+
+tok = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model.eval()
+
+def respond(msg, history, max_new, temp):
+    chat = []
+    for u, a in history:
+        chat += [{"role": "user", "content": u}, {"role": "assistant", "content": a}]
+    chat.append({"role": "user", "content": msg})
+
+    input_ids = tok.apply_chat_template(chat, return_tensors="pt", add_generation_prompt=True).to(model.device)
+
+    streamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True)
+    gen_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        max_new_tokens=max_new,
+        temperature=temp,
+        do_sample=temp > 0,
+    )
+    Thread(target=model.generate, kwargs=gen_kwargs).start()
+
+    out = ""
+    for token in streamer:
+        out += token
+        yield out
+
+EXAMPLES = [
+    ["Write a Python function to find all prime numbers up to n using the Sieve of Eratosthenes."],
+    ["Implement a binary search tree with insert and search methods in Python."],
+    ["Write a decorator that caches function results (memoization)."],
+]
+
+with gr.Blocks(title="kimi-coder-135m") as demo:
+    gr.Markdown(
+        """
+## 🤖 kimi-coder-135m
+SmolLM2-135M fine-tuned on 15k coding samples distilled from KIMI-K2.5.  
+Model: [yava-code/kimi-coder-135m](https://huggingface.co/yava-code/kimi-coder-135m)
+        """
+    )
+    chatbot = gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Slider(64, 1024, value=512, label="Max new tokens"),
+            gr.Slider(0, 1, value=0.3, step=0.05, label="Temperature"),
+        ],
+        examples=EXAMPLES,
+        title="",
+    )
+
+demo.launch()