Live demo: 4-language LeetCode coder suite

app.py

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+import spaces
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+BASE_MODEL = "unsloth/Qwen2.5-Coder-7B-Instruct"
+ADAPTERS = {
+    "Python": "AmareshHebbar/leetcode-python-qwen25-coder-7b",
+    "Java": "AmareshHebbar/leetcode-java-qwen25-coder-7b",
+    "C++": "AmareshHebbar/leetcode-cpp-qwen25-coder-7b",
+    "JavaScript": "AmareshHebbar/leetcode-javascript-qwen25-coder-7b",
+}
+
+print("Loading base model...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto",
+)
+
+print("Attaching adapters...")
+model = PeftModel.from_pretrained(base_model, ADAPTERS["Python"], adapter_name="Python")
+for lang, repo in ADAPTERS.items():
+    if lang != "Python":
+        model.load_adapter(repo, adapter_name=lang)
+model.eval()
+
+EXAMPLES = [
+    ["Merge k sorted linked lists into one sorted list.", "Divide and conquer / heap", "Python"],
+    ["Given a set of non-overlapping intervals, insert a new interval and merge as needed.", "Sorting / interval merge", "JavaScript"],
+    ["Find the length of the longest increasing path in a matrix.", "DFS + memoization", "Java"],
+]
+
+@spaces.GPU(duration=60)
+def solve(problem: str, algorithm_tag: str, language: str):
+    model.set_adapter(language)
+    lang_name = "C++" if language == "C++" else language
+    system_prompt = (
+        f"You are an expert {lang_name} competitive programmer. Given a "
+        f"LeetCode-style problem statement and an algorithm tag, write a "
+        f"correct, efficient {lang_name} solution."
+    )
+    user_msg = f"Problem: {problem}"
+    if algorithm_tag.strip():
+        user_msg += f"\nAlgorithm: {algorithm_tag}"
+
+    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}]
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    outputs = model.generate(
+        **inputs, max_new_tokens=512, temperature=0.2, do_sample=True,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+
+
+with gr.Blocks(title="LeetCode Multi-Language Coder Suite") as demo:
+    gr.Markdown(
+        "# LeetCode Multi-Language Coder Suite\n"
+        "Qwen2.5-Coder-7B, QDoRA fine-tuned per language on execution-verified "
+        "LeetCode solutions. [Models & benchmarks](https://huggingface.co/collections/AmareshHebbar/leetcode-multi-language-coder-suite) · "
+        "[GitHub](https://github.com/amareshhebbar)"
+    )
+    with gr.Row():
+        with gr.Column():
+            problem = gr.Textbox(label="Problem statement", lines=4,
+                                  placeholder="Given an array of integers nums and an integer target...")
+            tag = gr.Textbox(label="Algorithm tag (optional)", placeholder="Hash Map")
+            language = gr.Dropdown(list(ADAPTERS.keys()), value="Python", label="Language")
+            run = gr.Button("Generate solution", variant="primary")
+        with gr.Column():
+            output = gr.Code(label="Generated solution", language="python")
+
+    gr.Examples(examples=EXAMPLES, inputs=[problem, tag, language])
+    run.click(solve, inputs=[problem, tag, language], outputs=output)
+
+demo.launch()

requirements.txt

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+transformers
+peft
+accelerate
+torch
+spaces
+gradio