chore: move all Llama Stack types from llama-models to llama-stack (#1098)

llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.

This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279

## Test Plan

Ensure all `llama` CLI `model` sub-commands work:

```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```

Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```

Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs

Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.

```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```

llama_stack/models/llama/prompt_format.py

@@ -0,0 +1,204 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# top-level folder for each specific model found within the models/ directory at
+# the top-level of this source tree.
+
+import json
+import textwrap
+from pathlib import Path
+from typing import List
+
+from llama_models.datatypes import (
+    RawContent,
+    RawMediaItem,
+    RawMessage,
+    RawTextItem,
+    StopReason,
+    ToolCall,
+    ToolPromptFormat,
+)
+from pydantic import BaseModel, Field
+
+from .llama3.interface import LLama31Interface
+from .llama3.template_data import (
+    system_message_builtin_code_only,
+    system_message_builtin_tools_only,
+    system_message_custom_tools_only,
+)
+
+
+class TextCompletionContent(BaseModel):
+    content: RawContent = ""
+
+
+class UseCase(BaseModel):
+    title: str = ""
+    description: str = ""
+    dialogs: List[List[RawMessage] | TextCompletionContent | str] = Field(default_factory=list)
+    notes: str = ""
+    tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json
+
+    def md_format(self):
+        section = textwrap.dedent(
+            """
+            ## {title}
+
+            {description}
+
+            {dialogs_text}
+            {notes}
+
+            """
+        )
+        return section.lstrip()
+
+    def dialogs_to_text(self, generator) -> str:
+        def _code_block(text):
+            return f"```\n{text}\n```"
+
+        text = ""
+        for dialog in self.dialogs:
+            if isinstance(dialog, str):
+                text += dialog
+                text += "\n\n"
+                continue
+
+            elif isinstance(dialog, TextCompletionContent):
+                input_tokens, output_tokens = generator.text_completion_raw(
+                    dialog.content,
+                    max_gen_len=64,
+                    temperature=0.1,
+                    top_p=0.95,
+                )
+            else:
+                input_tokens, output_tokens = generator.chat_completion_raw(
+                    dialog,
+                    max_gen_len=512,
+                    temperature=0.0,
+                    top_p=0.95,
+                    tool_prompt_format=self.tool_prompt_format,
+                )
+            text += "##### Input Prompt Format\n"
+
+            # FIXME: This is added to undo the hack in chat_formatter where
+            # vision tokens are replaced with 128256.
+            input_tokens = [generator.formatter.vision_token if t == 128256 else t for t in input_tokens]
+
+            text += _code_block(generator.tokenizer.decode(input_tokens))
+            # TODO: Figure out if "↵" needs to be added for newlines or end or some indication
+            text += "\n\n"
+            text += "##### Model Response Format\n"
+            text += _code_block(generator.tokenizer.decode(output_tokens))
+            text += "\n\n"
+
+        return text
+
+    def to_text(self, generator):
+        section = self.md_format()
+        dialogs_text = self.dialogs_to_text(generator)
+        notes = f"##### Notes\n{self.notes}" if self.notes else ""
+        section = section.format(
+            title=self.title,
+            description=self.description,
+            dialogs_text=dialogs_text,
+            notes=notes,
+        )
+        return section
+
+
+def llama3_1_builtin_tool_call_dialog(tool_prompt_format=ToolPromptFormat.json):
+    interface = LLama31Interface(tool_prompt_format)
+
+    messages = interface.system_messages(**system_message_builtin_tools_only())
+    messages += interface.user_message(content="Search the web for the latest price of 1oz gold?")
+
+    return messages
+
+
+def llama3_1_builtin_code_interpreter_dialog(tool_prompt_format=ToolPromptFormat.json):
+    interface = LLama31Interface(tool_prompt_format)
+
+    messages = interface.system_messages(**system_message_builtin_code_only())
+    messages += interface.user_message(
+        content="Write code to check if number is prime. Use it to verify if number 7 is prime"
+    )
+
+    return messages
+
+
+def llama3_1_builtin_tool_call_with_image_dialog(
+    tool_prompt_format=ToolPromptFormat.json,
+):
+    this_dir = Path(__file__).parent
+    with open(this_dir / "llama3/dog.jpg", "rb") as f:
+        img = f.read()
+
+    interface = LLama31Interface(tool_prompt_format)
+
+    messages = interface.system_messages(**system_message_builtin_tools_only())
+    messages += interface.user_message(content=[RawMediaItem(data=img), RawTextItem(text="What is this dog breed?")])
+    messages += interface.assistant_response_messages(
+        "Based on the description of the dog in the image, it appears to be a small breed dog, possibly a terrier mix",
+        StopReason.end_of_turn,
+    )
+    messages += interface.user_message("Search the web for some food recommendations for the indentified breed")
+    return messages
+
+
+def llama3_1_custom_tool_call_dialog(tool_prompt_format=ToolPromptFormat.json):
+    interface = LLama31Interface(tool_prompt_format)
+
+    messages = interface.system_messages(**system_message_custom_tools_only())
+    messages += interface.user_message(content="Use tools to get latest trending songs")
+    return messages
+
+
+def llama3_1_e2e_tool_call_dialog(tool_prompt_format=ToolPromptFormat.json):
+    tool_response = json.dumps(["great song1", "awesome song2", "cool song3"])
+    interface = LLama31Interface(tool_prompt_format)
+
+    messages = interface.system_messages(**system_message_custom_tools_only())
+    messages += interface.user_message(content="Use tools to get latest trending songs")
+    messages.append(
+        RawMessage(
+            role="assistant",
+            content="",
+            stop_reason=StopReason.end_of_message,
+            tool_calls=[
+                ToolCall(
+                    call_id="call_id",
+                    tool_name="trending_songs",
+                    arguments={"n": "10", "genre": "latest"},
+                )
+            ],
+        ),
+    )
+    messages.append(
+        RawMessage(
+            role="assistant",
+            content=tool_response,
+        )
+    )
+    return messages
+
+
+def llama3_2_user_assistant_conversation():
+    return UseCase(
+        title="User and assistant conversation",
+        description="Here is a regular multi-turn user assistant conversation and how its formatted.",
+        dialogs=[
+            [
+                RawMessage(role="system", content="You are a helpful assistant"),
+                RawMessage(role="user", content="Who are you?"),
+            ]
+        ],
+        notes="This format is unchanged from Llama3.1",
+    )