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README.md

@@ -1,13 +1,26 @@
 ---
 title: Junior Associate
-emoji: 🦀
-colorFrom: yellow
-colorTo: yellow
+emoji: 📄
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: 4.36.0
 app_file: app.py
 pinned: false
+license: apache-2.0
+hardware: zero-a10g
+short_description: Qwen-3B fine-tuned for contract review in IRAC format
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Junior Associate · Contract Review in IRAC
+
+Qwen 2.5-3B-Instruct fine-tuned with LoRA on 80 hand-crafted contract-review memos. Every reply produces the firm's IRAC house format with mandatory top + bottom disclaimers.
+
+Built for the Curious PM "Stay Curious" session on fine-tuning. Trained via the [`hf-llm-trainer`](https://huggingface.co/blog/hf-skills-training) Claude skill on Hugging Face Jobs.
+
+## Model
+
+- Base: `Qwen/Qwen2.5-3B-Instruct`
+- Adapter: [`Curious-PM/lexwell-contract-irac-qwen2.5-3b-lora`](https://huggingface.co/Curious-PM/lexwell-contract-irac-qwen2.5-3b-lora)
+- LoRA: `r=16`, `alpha=32`, attention modules only
+- Training: 10 epochs, batch 2 × grad-accum 2, lr 2e-4, ~7 min on A10G

app.py

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+"""Junior Associate — contract review in IRAC format.
+
+Fine-tuned Qwen 2.5-3B-Instruct with a LoRA adapter trained on 80 hand-crafted
+contract-review memos. Hosted on Hugging Face Spaces with ZeroGPU.
+"""
+import torch
+import spaces
+import gradio as gr
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+BASE = "Qwen/Qwen2.5-3B-Instruct"
+LORA = "Curious-PM/lexwell-contract-irac-qwen2.5-3b-lora"
+
+SYSTEM_PROMPT = (
+    "You are an associate at Lexwell Advisors, a contract-review advisory "
+    "firm for SMBs. Reply in Lexwell's house IRAC format with required top "
+    "and bottom disclaimers."
+)
+
+# Load model + adapter once at startup
+print(f"Loading base model {BASE} ...")
+tokenizer = AutoTokenizer.from_pretrained(BASE)
+base_model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16)
+print(f"Loading LoRA adapter {LORA} ...")
+model = PeftModel.from_pretrained(base_model, LORA)
+model.eval()
+print("Model ready.")
+
+
+@spaces.GPU(duration=60)
+def generate_reply(question):
+    if not question or not question.strip():
+        return "_Type a contract question first._"
+
+    msgs = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": question.strip()},
+    ]
+    inputs = tokenizer.apply_chat_template(
+        msgs, return_tensors="pt", add_generation_prompt=True
+    ).to("cuda")
+    model.to("cuda")
+    with torch.no_grad():
+        out = model.generate(
+            inputs,
+            max_new_tokens=600,
+            do_sample=False,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    reply = tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)
+    return reply
+
+
+CSS = """
+.gradio-container { max-width: 980px !important; }
+#title { text-align: center; margin-bottom: 4px; font-weight: 800; letter-spacing: -1px; }
+#sub   { text-align: center; color: #6B6B6B; margin-bottom: 24px; font-size: 14.5px; }
+.output-box textarea, .output-box .markdown {
+    font-size: 14.5px !important; line-height: 1.6 !important;
+}
+"""
+
+EXAMPLES = [
+    "Our SaaS vendor wants us to sign: 'Customer grants Vendor a perpetual, irrevocable license to use Customer Data for any purpose, including ML training.' Is this normal?",
+    "Their non-compete is 2 years, all of California. Is that enforceable on a new hire?",
+    "Our enterprise customer wants source code escrow with release on bankruptcy, material breach, or product discontinuation. Push back?",
+    "We're hiring our first UK employee. Should we use an Employer of Record service or set up a UK subsidiary?",
+    "A vendor's MSA caps liability at $1M for any claim. Our annual fees are $500K and they hold our customer database. Is the cap reasonable?",
+]
+
+with gr.Blocks(title="Junior Associate · Contract Review (IRAC)", css=CSS, theme=gr.themes.Soft()) as demo:
+    gr.HTML('<h1 id="title">Junior Associate</h1>')
+    gr.HTML(
+        '<div id="sub">Qwen 2.5-3B fine-tuned on 80 hand-crafted contract-review memos. '
+        'Every reply: top disclaimer, IRAC analysis, numbered redlines, bottom disclaimer, sign-off. '
+        'Built with the <a href="https://huggingface.co/blog/hf-skills-training" target="_blank">'
+        '<code>hf-llm-trainer</code></a> Claude skill on Hugging Face Jobs.</div>'
+    )
+
+    question = gr.Textbox(
+        label="Paste a contract clause or ask a question",
+        placeholder="e.g. Their MSA caps liability at $1M. Is that reasonable?",
+        lines=3,
+    )
+    submit = gr.Button("Ask the Junior Associate ▸", variant="primary", size="lg")
+
+    output = gr.Markdown(
+        value="_The reply will appear here. First request takes ~10s as the GPU warms up._",
+        elem_classes="output-box",
+    )
+
+    submit.click(fn=generate_reply, inputs=question, outputs=output)
+    question.submit(fn=generate_reply, inputs=question, outputs=output)
+
+    gr.Examples(examples=EXAMPLES, inputs=question, label="Try one of these")
+
+    gr.HTML(
+        '<div style="text-align: center; color: #888; font-size: 12px; margin-top: 32px;">'
+        'Model: <a href="https://huggingface.co/Curious-PM/lexwell-contract-irac-qwen2.5-3b-lora" target="_blank">'
+        'Curious-PM/lexwell-contract-irac-qwen2.5-3b-lora</a>'
+        ' · Built for <a href="https://curious.pm" target="_blank">Curious PM</a> &middot; Stay Curious session on fine-tuning'
+        '</div>'
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch>=2.4.0
+transformers>=4.45.0
+peft>=0.13.0
+accelerate>=1.0.0
+gradio>=4.36.0
+spaces