Update README.md

README.md

@@ -1,35 +1,38 @@
-# minicache — tiny disk cache for ML / research code.
+## minicache — tiny disk cache for ML / research code.
 
-Wraps function calls and stores returns on disk (gzip + cloudpickle). Solves
+This 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.
+- *Loaded models can't be hashed*. So we use a arg blacklist (`exclude=["model", "tok"]`).
+  Here, excluded args pass through to the function but never enter the cache key.
+- *Tensors / pandas / closures can't be picked** → we use cloudpickle which extends to many more objects.
+- *Pickle files grow large* → gzip on disk save 20-50%
 
 ## Quick use
 
+Install
+
+```sh
+uv add git+https://github.com/wassname/minicache.git
+```
+
 ```py
-  from minicache import cached, cache_call
+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)
+# 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)
+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")
+# 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