Create README.md

README.md

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+---
+# fill these to enable discovery buttons on HF
+license: apache-2.0   # ← set to the SAME license as your base model
+language:
+- en
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-3B-Instruct   # ← put the exact base you fine-tuned from
+tags:
+- domain-finetuning
+- finance
+- qwen
+- tgi
+- vercel
+---
+
+# FinSight LLM (Domain-FT Qwen 3B)
+
+**TL;DR**: A domain-tuned finance Q&A model (Qwen 3B) for ratios, filings, and valuation topics.  
+Deployed via **Text Generation Inference (TGI)**; frontend: Next.js (Vercel).
+
+## Intended use
+- Educational finance Q&A, ratio explanations, simplified filings summaries.
+- Not for investment advice or execution. See *Limitations & Safety*.
+
+## How to use
+
+### Python (Transformers)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "willckim/domain-ft-qwen3b"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+prompt = "Explain PEG vs P/E with a 1-liner example."
+x = tok(prompt, return_tensors="pt").to(model.device)
+y = model.generate(**x, max_new_tokens=256, temperature=0.2)
+print(tok.decode(y[0], skip_special_tokens=True))