comments + docs

llms/README.md

@@ -20,6 +20,31 @@ The `mlx-lm` package also has:
 - [Merging models](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/MERGE.md)
 - [HTTP model serving](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/SERVER.md)
 
+### Quick Start
+
+To generate text with an LLM use:
+
+```bash
+mlx_lm.generate --prompt "Hi!"
+```
+
+To chat with an LLM use:
+
+```bash
+mlx_lm.chat
+```
+
+This will give you a chat REPL that you can use to interact with the LLM. The
+chat context is preserved during the lifetime of the REPL.
+
+Commands in `mlx-lm` typically take command line options which let you specify
+the model, sampling parameters, and more. Use `-h` to see a list of available
+options for a command, e.g.:
+
+```bash
+mlx_lm.generate -h
+```
+
 ### Python API
 
 You can use `mlx-lm` as a module:
@@ -138,7 +163,7 @@ mlx_lm.convert \
 
 ### Long Prompts and Generations 
 
-MLX LM has some tools to scale efficiently to long prompts and generations:
+`mlx-lm` has some tools to scale efficiently to long prompts and generations:
 
 - A rotating fixed-size key-value cache.
 - Prompt caching
@@ -178,7 +203,7 @@ for more usage details.
 
 ### Supported Models
 
-MLX LM supports thousands of Hugging Face format LLMs. If the model you want to
+`mlx-lm` supports thousands of Hugging Face format LLMs. If the model you want to
 run is not supported, file an
 [issue](https://github.com/ml-explore/mlx-examples/issues/new) or better yet,
 submit a pull request.

llms/mlx_lm/chat.py

@@ -56,7 +56,7 @@ def main():
         tokenizer_config={"trust_remote_code": True},
     )
 
-    print(f"Starting chat sessiong with {args.model}. To exit, enter 'q'.")
+    print(f"[INFO] Starting chat sessiong with {args.model}. To exit, enter 'q'.")
     prompt_cache = make_prompt_cache(model, args.max_kv_size)
     while True:
         query = input(">> ")

llms/mlx_lm/generate.py

@@ -14,6 +14,7 @@ DEFAULT_MAX_TOKENS = 100
 DEFAULT_TEMP = 0.6
 DEFAULT_TOP_P = 1.0
 DEFAULT_SEED = 0
+DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit"
 
 
 def str2bool(string):
@@ -27,6 +28,7 @@ def setup_arg_parser():
         "--model",
         type=str,
         help="The path to the local model directory or Hugging Face repo.",
+        default=DEFAULT_MODEL,
     )
     parser.add_argument(
         "--adapter-path",

llms/mlx_lm/models/cache.py

@@ -44,12 +44,11 @@ def save_prompt_cache(
         metadata (Optional[Dict[str, str]]): Optional metadata to save along
             with model state..
     """
-    cache_data, cache_info = zip(*(c.state for c in cache))
+    cache_data = [c.state for c in cache]
+    cache_info = [c.meta_state if hasattr(c, "meta_state") else "" for c in cache]
     cache_data = dict(tree_flatten(cache_data))
     cache_classes = [type(c).__name__ for c in cache]
-    cache_metadata = [cache_classes, cache_info]
-    if metadata:
-        cache_metadata.append(metadata)
+    cache_metadata = [cache_classes, cache_info, metadata or ""]
     cache_metadata = dict(tree_flatten(cache_metadata))
     mx.save_safetensors(file_name, cache_data, cache_metadata)
 
@@ -70,12 +69,14 @@ def load_prompt_cache(file_name, return_metadata=False):
     arrays, cache_metadata = mx.load(file_name, return_metadata=True)
     arrays = tree_unflatten(list(arrays.items()))
     cache_metadata = tree_unflatten(list(cache_metadata.items()))
-    classes, info = cache_metadata[:2]
+    classes, info, metadata = cache_metadata
     cache = [globals()[c]() for c in classes]
-    for c, *state in zip(cache, arrays, info):
+    for c, state, meta_state in zip(cache, arrays, info):
         c.state = state
+        if hasattr(c, "meta_state"):
+            c.meta_state = meta_state
     if return_metadata:
-        return cache, cache_metadata[2]
+        return cache, metadata
     return cache
 
 
@@ -114,16 +115,16 @@ class KVCache:
     @property
     def state(self):
         if self.offset == self.keys.shape[2]:
-            return (self.keys, self.values), ""
+            return self.keys, self.values
         else:
             return (
                 self.keys[..., : self.offset, :],
                 self.values[..., : self.offset, :],
-            ), ""
+            )
 
     @state.setter
     def state(self, v):
-        self.keys, self.values = v[0]
+        self.keys, self.values = v
         self.offset = self.keys.shape[2]
 
 
@@ -236,20 +237,25 @@ class RotatingKVCache:
     @property
     def state(self):
         if self.offset < self.keys.shape[2]:
-            kv_state = (self.keys[..., : self.offset], self.values[..., : self.offset])
+            return self.keys[..., : self.offset, :], self.values[..., : self.offset, :]
         else:
-            kv_state = (self.keys, self.values)
-        extra_state = tuple(
-            map(str, (self.keep, self.max_size, self.step, self.offset, self._idx))
-        )
-        return kv_state, extra_state
+            return self.keys, self.values
 
     @state.setter
     def state(self, v):
-        self.keys, self.values = v[0]
+        self.keys, self.values = v
+
+    @property
+    def meta_state(self):
+        return tuple(
+            map(str, (self.keep, self.max_size, self.step, self.offset, self._idx))
+        )
+
+    @meta_state.setter
+    def meta_state(self, v):
         self.keep, self.max_size, self.step, self.offset, self._idx = map(
             int,
-            v[1],
+            v,
         )
 
 
@@ -267,10 +273,6 @@ class MambaCache:
     def state(self):
         return self.cache
 
-    @property
-    def state(self):
-        return self.cache, ""
-
     @state.setter
     def state(self, v):
-        self.cache = v[0]
+        self.cache = v

llms/mlx_lm/utils.py

@@ -240,7 +240,7 @@ def generate_step(
 
     while y.size > prefill_step_size:
         model(y[:prefill_step_size][None], cache=cache)
-        mx.eval([c.state[0] for c in cache])
+        mx.eval([c.state for c in cache])
         y = y[prefill_step_size:]
 
     y, logprobs = _step(y)

llms/tests/test_prompt_cache.py

@@ -40,8 +40,8 @@ class TestPromptCache(unittest.TestCase):
         self.assertTrue(len(cache), len(loaded_cache))
         for c, lc in zip(cache, loaded_cache):
             self.assertEqual(c.offset, lc.offset)
-            self.assertTrue(mx.array_equal(c.state[0][0], lc.state[0][0]))
-            self.assertTrue(mx.array_equal(c.state[0][1], lc.state[0][1]))
+            self.assertTrue(mx.array_equal(c.state[0], lc.state[0]))
+            self.assertTrue(mx.array_equal(c.state[1], lc.state[1]))
 
         # Test with metadata
         cache_file = os.path.join(self.test_dir, "prompt_cache.safetensors")
@@ -67,8 +67,8 @@ class TestPromptCache(unittest.TestCase):
             self.assertEqual(c.keep, lc.keep)
             self.assertEqual(c.max_size, lc.max_size)
             self.assertEqual(c.step, lc.step)
-            self.assertTrue(mx.array_equal(c.state[0][0], lc.state[0][0]))
-            self.assertTrue(mx.array_equal(c.state[0][1], lc.state[0][1]))
+            self.assertTrue(mx.array_equal(c.state[0], lc.state[0]))
+            self.assertTrue(mx.array_equal(c.state[1], lc.state[1]))
 
         # Do a couple single token updates to get a rotation
         for _ in range(2):