[tutorial] Added multi-model tutorial and refactored LLM model class (

#511)

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## Summary

<!-- Please give a short summary of the change and the problem this
solves. -->

## Related issues

<!-- For example: "Closes #1234" -->

## Checks

- [ ] `make lint`: I've run `make lint` to lint the changes in this PR.
- [ ] `make test`: I've made sure the tests (`make test-cpu` or `make
test`) are passing.
- Additional tests:
   - [ ] Benchmark tests (when contributing new models)
   - [ ] GPU/HW tests

examples/tutorials/04-multiple-models/summarize_audio.py

@@ -0,0 +1,60 @@
+import sys
+from pathlib import Path
+
+import rich.console
+
+from nos.client import Client
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Summarize audio file.")
+    parser.add_argument("--filename", type=str, help="Audio file to summarize.")
+    args = parser.parse_args()
+
+    console = rich.console.Console()
+    path = Path(args.filename)
+
+    # Create a client
+    address = "[::]:50051"
+    print(f"Connecting to client at {address} ...")
+    client = Client(address)
+    client.WaitForServer()
+
+    # Transcribe with Whisper
+    model_id = "distil-whisper/distil-small.en"
+    model = client.Module(model_id)
+    console.print()
+    console.print(f"[bold white]Transcribe with [yellow]{model_id}[/yellow].[/bold white]")
+
+    # Transcribe the audio file and print the text
+    transcription_text = ""
+    print(f"Transcribing audio file: {path}")
+    with client.UploadFile(path) as remote_path:
+        response = model.transcribe(path=remote_path, batch_size=8)
+        for item in response["chunks"]:
+            transcription_text += item["text"]
+            sys.stdout.write(item["text"])
+            sys.stdout.flush()
+    print()
+
+    # Summarize the transcription with LLMs
+    model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+    llm = client.Module(model_id)
+    console.print()
+    console.print("[bold white]Summarize with [yellow]TinyLlama/TinyLlama-1.1B-Chat-v1.0[/yellow].[/bold white]")
+
+    prompt = f"""
+    You are a useful transcribing assistant.
+    Summarize the following text concisely with key points.
+    Keep the sentences short, highlight key concepts in each bullet starting with a hyphen.
+
+    {transcription_text}
+    """
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    for response in llm.chat(messages=messages, max_new_tokens=1024, _stream=True):
+        sys.stdout.write(response)
+        sys.stdout.flush()

nos/models/__init__.py

@@ -12,7 +12,7 @@
 from .blip import BLIP  # noqa: F401
 from .clip import CLIP  # noqa: F401
 from .faster_rcnn import FasterRCNN  # noqa: F401
-from .llama2_chat import Llama2Chat  # noqa: F401
+from .llm import LLM  # noqa: F401
 from .monodepth import MonoDepth  # noqa: F401
 from .owlvit import OwlViT  # noqa: F401
 from .sam import SAM

nos/models/llama2_chat.py → nos/models/llm.py

@@ -19,7 +19,7 @@
 
 
 @dataclass(frozen=True)
-class Llama2ChatConfig(HuggingFaceHubConfig):
+class LLMConfig(HuggingFaceHubConfig):
     """Llama2 chat model configuration."""
 
     max_new_tokens: int = 2048
@@ -41,52 +41,56 @@ class Llama2ChatConfig(HuggingFaceHubConfig):
     """Chat template to use for the model."""
 
 
-class Llama2Chat:
+class LLM:
     configs = {
-        "meta-llama/Llama-2-7b-chat-hf": Llama2ChatConfig(
+        "meta-llama/Llama-2-7b-chat-hf": LLMConfig(
             model_name="meta-llama/Llama-2-7b-chat-hf",
             compute_dtype="float16",
             needs_auth=True,
             chat_template=LLAMA2_CHAT_TEMPLATE,
         ),
-        "meta-llama/Llama-2-13b-chat-hf": Llama2ChatConfig(
+        "meta-llama/Llama-2-13b-chat-hf": LLMConfig(
             model_name="meta-llama/Llama-2-13b-chat-hf",
             compute_dtype="float16",
             needs_auth=True,
             chat_template=LLAMA2_CHAT_TEMPLATE,
         ),
-        "meta-llama/Llama-2-70b-chat-hf": Llama2ChatConfig(
+        "meta-llama/Llama-2-70b-chat-hf": LLMConfig(
             model_name="meta-llama/Llama-2-70b-chat-hf",
             compute_dtype="float16",
             needs_auth=True,
             chat_template=LLAMA2_CHAT_TEMPLATE,
         ),
-        "HuggingFaceH4/zephyr-7b-beta": Llama2ChatConfig(
+        "HuggingFaceH4/zephyr-7b-beta": LLMConfig(
             model_name="HuggingFaceH4/zephyr-7b-beta",
             compute_dtype="float16",
         ),
-        "HuggingFaceH4/tiny-random-LlamaForCausalLM": Llama2ChatConfig(
+        "HuggingFaceH4/tiny-random-LlamaForCausalLM": LLMConfig(
             model_name="HuggingFaceH4/tiny-random-LlamaForCausalLM",
             compute_dtype="float16",
         ),
-        "NousResearch/Yarn-Mistral-7b-128k": Llama2ChatConfig(
+        "NousResearch/Yarn-Mistral-7b-128k": LLMConfig(
             model_name="NousResearch/Yarn-Mistral-7b-128k",
             compute_dtype="float16",
             additional_kwargs={"use_flashattention_2": True, "trust_remote_code": True},
         ),
-        "mistralai/Mistral-7B-Instruct-v0.2": Llama2ChatConfig(
+        "mistralai/Mistral-7B-Instruct-v0.2": LLMConfig(
             model_name="mistralai/Mistral-7B-Instruct-v0.2",
             compute_dtype="float16",
         ),
+        "TinyLlama/TinyLlama-1.1B-Chat-v1.0": LLMConfig(
+            model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            compute_dtype="float16",
+        ),
     }
 
     def __init__(self, model_name: str = "HuggingFaceH4/zephyr-7b-beta"):
         from nos.logging import logger
 
         try:
-            self.cfg = Llama2Chat.configs[model_name]
+            self.cfg = LLM.configs[model_name]
         except KeyError:
-            raise ValueError(f"Invalid model_name: {model_name}, available models: {Llama2ChatConfig.configs.keys()}")
+            raise ValueError(f"Invalid model_name: {model_name}, available models: {LLMConfig.configs.keys()}")
 
         self.device_str = "cuda" if torch.cuda.is_available() else "cpu"
         self.device = torch.device(self.device_str)
@@ -150,19 +154,19 @@ def chat(
                 start_t = time.perf_counter()
             if idx > 0:
                 self.logger.debug(
-                    f"""tok/s={idx / (time.perf_counter() - start_t):.2f}, """
-                    f"""memory={torch.cuda.memory_allocated(device=self.model.device) / 1024 ** 2:.2f} MB, """
-                    f"""allocated={torch.cuda.max_memory_allocated(device=self.model.device) / 1024 ** 2:.2f} MB, """
-                    f"""peak={torch.cuda.max_memory_reserved(device=self.model.device) / 1024 ** 2:.2f} MB, """
+                    f"""tok/s={idx / (time.perf_counter() - start_t):.1f}, """
+                    f"""memory={torch.cuda.memory_allocated(device=self.model.device) / 1024 ** 2:.1f} MB, """
+                    f"""allocated={torch.cuda.max_memory_allocated(device=self.model.device) / 1024 ** 2:.1f} MB, """
+                    f"""peak={torch.cuda.max_memory_reserved(device=self.model.device) / 1024 ** 2:.1f} MB, """
                 )
 
 
-for model_name in Llama2Chat.configs:
-    cfg = Llama2Chat.configs[model_name]
+for model_name in LLM.configs:
+    cfg = LLM.configs[model_name]
     hub.register(
         model_name,
         TaskType.TEXT_GENERATION,
-        Llama2Chat,
+        LLM,
         init_args=(model_name,),
         method="chat",
     )

nos/models/whisper.py

@@ -39,6 +39,10 @@ class Whisper:
         "openai/whisper-large-v3": WhisperConfig(
             model_name="openai/whisper-large-v3",
         ),
+        "distil-whisper/distil-small.en": WhisperConfig(
+            model_name="distil-whisper/distil-small.en",
+            torch_dtype="float16",
+        ),
         "distil-whisper/distil-medium.en": WhisperConfig(
             model_name="distil-whisper/distil-medium.en",
             torch_dtype="float16",

tests/models/test_llama2_chat.py → tests/models/test_llm.py

@@ -1,21 +1,21 @@
 """Llama2 Chat model tests and benchmarks."""
 import pytest
 
-from nos.models import Llama2Chat
+from nos.models import LLM
 from nos.test.utils import skip_if_no_torch_cuda
 
 
-SYSTEM_PROMPT = "You are NOS chat, a Llama 2 large language model (LLM) agent hosted by Autonomi AI."
+SYSTEM_PROMPT = "You are NOS chat, a large language model (LLM) agent hosted by Autonomi AI."
 
 
 @pytest.fixture(scope="module")
 def model():
-    MODEL_NAME = "HuggingFaceH4/tiny-random-LlamaForCausalLM"
-    yield Llama2Chat(model_name=MODEL_NAME)
+    MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+    yield LLM(model_name=MODEL_NAME)
 
 
 @skip_if_no_torch_cuda
-def test_llama2_chat(model):
+def test_streaming_chat(model):
     messages = [
         {"role": "system", "content": SYSTEM_PROMPT},
         {"role": "user", "content": "What is the meaning of life?"},