Create README.md for minicache project

Added a README file with project overview and usage instructions.

README.md

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+# minicache — tiny disk cache for ML / research code.
+
+Wraps function calls and stores returns on disk (gzip + cloudpickle). Solves
+the four pain points that stdlib `functools.lru_cache + pickle` and existing
+function-cache libraries (anycache, cachier) hit on ML code:
+
+- **Loaded models can't be hashed** → arg blacklist (`exclude=["model", "tok"]`).
+  Excluded args pass through to the function but never enter the cache key.
+- **Tensors / pandas / closures break stdlib pickle** → cloudpickle backend.
+- **Pickle files grow large** → gzip on disk (~3× smaller, free).
+- **"Function source changed → invalidate" causes false invalidations on
+  reformat** → caller bumps an explicit `state` string when behavior actually
+  changes. No AST hashing magic.
+
+## Quick use
+
+```py
+  from minicache import cached, cache_call
+
+  # 1. Decorator: hashes (state, included args). Excludes drop out of key.
+  @cached("eval", cachedir="out/cache",
+          state_fn=lambda *, model_id, **_: f"{model_id}|nf4|r00+r02",
+          exclude=["model", "tok"])
+  def run_eval(model, tok, *, model_id, name, batch_size):
+      return tinymfv_evaluate(model, tok, name=name, batch_size=batch_size)
+
+  report = run_eval(model, tok, model_id="qwen-27b", name="classic", batch_size=16)
+
+  # 2. Explicit key: no introspection, you compose the key
+  key = "qwen-27b|nf4|r00+r02|eval|classic|bs=16"
+  report = cache_call("eval", key, lambda: tinymfv_evaluate(model, tok, ...),
+                      cachedir="out/cache")
+```
+
+See also
+- anycache https://github.com/c0fec0de/anycache
+- cachier https://github.com/python-cachier/cachier#working-with-unhashable-arguments