More cache improvements (#1015)

* fix rotating kv cache for chat use case

* reorg + fixes to caching, unify prompt caching across types and use cases for e.g. caching during a chat

* nit in chat

* fix tests

* fix tests

* fix tests

* docs

* chat command

* comments + docs

* Define meta_state on all Cache implementations

* fixes + trim_prompt_cache api

* fix default model

---------

Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>

llms/mlx_lm/examples/chat.py

@@ -0,0 +1,53 @@
+# Copyright © 2024 Apple Inc.
+
+"""
+An example of a multi-turn chat with prompt caching.
+"""
+
+from mlx_lm import generate, load
+from mlx_lm.models.cache import load_prompt_cache, make_prompt_cache, save_prompt_cache
+
+model, tokenizer = load("mlx-community/Mistral-7B-Instruct-v0.3-4bit")
+
+# Make the initial prompt cache for the model
+prompt_cache = make_prompt_cache(model)
+
+# User turn
+prompt = "Hi my name is <Name>."
+messages = [{"role": "user", "content": prompt}]
+prompt = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+# Assistant response
+response = generate(
+    model,
+    tokenizer,
+    prompt=prompt,
+    verbose=True,
+    temp=0.0,
+    prompt_cache=prompt_cache,
+)
+
+# User turn
+prompt = "What's my name?"
+messages = [{"role": "user", "content": prompt}]
+prompt = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+# Assistant response
+response = generate(
+    model,
+    tokenizer,
+    prompt=prompt,
+    verbose=True,
+    temp=0.0,
+    prompt_cache=prompt_cache,
+)
+
+# Save the prompt cache to disk to reuse it at a later time
+save_prompt_cache("mistral_prompt.safetensors", prompt_cache)
+
+# Load the prompt cache from disk
+prompt_cache = load_prompt_cache("mistral_prompt.safetensors")