[Lora] Fix generate (#282)

* fix generate

* update readme, fix test, better default

* nits

* typo

lora/README.md

@@ -110,7 +110,7 @@ For generation use:
 ```
 python lora.py --model <path_to_model> \
                --adapter-file <path_to_adapters.npz> \
-               --num-tokens 50 \
+               --max-tokens 50 \
                --prompt "table: 1-10015132-16
 columns: Player, No., Nationality, Position, Years in Toronto, School/Club Team
 Q: What is terrence ross' nationality

lora/lora.py

@@ -265,7 +265,7 @@ def train(model, train_set, val_set, optimizer, loss, tokenizer, args):
 def generate(model, prompt, tokenizer, args):
     print(args.prompt, end="", flush=True)
 
-    prompt = tokenizer.encode(args.prompt)
+    prompt = mx.array(tokenizer.encode(args.prompt))
 
     tokens = []
     skip = 0

whisper/README.md

@@ -19,8 +19,13 @@ Install [`ffmpeg`](https://ffmpeg.org/):
 brew install ffmpeg
 ```
 
-Next, download the Whisper PyTorch checkpoint and convert the weights to the
-MLX format. For example, to convert the `tiny` model use:
+> [!TIP]
+> Skip the conversion step by using pre-converted checkpoints from the Hugging
+> Face Hub. There are a few available in the [MLX
+> Community](https://huggingface.co/mlx-community) organization.
+
+To convert a model, first download the Whisper PyTorch checkpoint and convert
+the weights to the MLX format. For example, to convert the `tiny` model use:
 
 ```
 python convert.py --torch-name-or-path tiny --mlx-path mlx_models/tiny
@@ -34,13 +39,8 @@ To generate a 4-bit quantized model, use `-q`. For a full list of options:
 python convert.py --help
 ```
 
-By default, the conversion script will make the directory `mlx_models/tiny` and save
-the converted `weights.npz` and `config.json` there.
-
-> [!TIP]
-> Alternatively, you can also download a few converted checkpoints from the
-> [MLX Community](https://huggingface.co/mlx-community) organization on Hugging
-> Face and skip the conversion step.
+By default, the conversion script will make the directory `mlx_models/tiny`
+and save the converted `weights.npz` and `config.json` there.
 
 ### Run
 
@@ -52,6 +52,16 @@ import whisper
 text = whisper.transcribe(speech_file)["text"]
 ```
 
+Choose the model by setting `hf_path_or_repo`. For example:
+
+```python
+result = whisper.transcribe(speech_file, hf_path_or_repo="models/large")
+```
+
+This will load the model contained in `models/large`. The `hf_path_or_repo`
+can also point to an MLX-style Whisper model on the Hugging Face Hub. In this
+case, the model will be automatically downloaded.
+
 The `transcribe` function also supports word-level timestamps. You can generate
 these with:
 

whisper/test.py

@@ -189,7 +189,7 @@ class TestWhisper(unittest.TestCase):
 
     def test_transcribe(self):
         result = whisper.transcribe(
-            TEST_AUDIO, model_path=MLX_FP32_MODEL_PATH, fp16=False
+            TEST_AUDIO, path_or_hf_repo=MLX_FP32_MODEL_PATH, fp16=False
         )
         self.assertEqual(
             result["text"],
@@ -210,7 +210,7 @@ class TestWhisper(unittest.TestCase):
             return
 
         result = whisper.transcribe(
-            audio_file, model_path=MLX_FP32_MODEL_PATH, fp16=False
+            audio_file, path_or_hf_repo=MLX_FP32_MODEL_PATH, fp16=False
         )
         self.assertEqual(len(result["text"]), 10920)
         self.assertEqual(result["language"], "en")
@@ -313,9 +313,8 @@ class TestWhisper(unittest.TestCase):
 
     def test_transcribe_word_level_timestamps_confidence_scores(self):
         result = whisper.transcribe(
-            # TEST_AUDIO, model_path=MLX_FP32_MODEL_PATH, word_timestamps=True, fp16=False
             TEST_AUDIO,
-            model_path=MLX_FP16_MODEL_PATH,
+            path_or_hf_repo=MLX_FP16_MODEL_PATH,
             word_timestamps=True,
         )
 

whisper/whisper/transcribe.py

@@ -62,7 +62,7 @@ class ModelHolder:
 def transcribe(
     audio: Union[str, np.ndarray, mx.array],
     *,
-    path_or_hf_repo: str = "mlx_models",
+    path_or_hf_repo: str = "mlx-community/whisper-tiny",
     verbose: Optional[bool] = None,
     temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
     compression_ratio_threshold: Optional[float] = 2.4,