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Merge branch 'main' into watsonx-infer-fix

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Sajikumar JS 2025-04-26 18:31:59 +05:30
commit 4884c62190
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1
llama_stack/models/llama/llama4/chat_format.py
View file

@ -303,6 +303,7 @@ class ChatFormat:
arguments_json=json.dumps(tool_arguments),
)
)
content = ""
return RawMessage(
role="assistant",

62
llama_stack/models/llama/llama4/prompt_format.md
View file

@ -64,7 +64,7 @@ This example passes an image that is smaller than the tile size, to show the til
##### Model Response Format
```
The image depicts a dog standing on a skateboard, with its front paws positioned on the board and its back paws hanging off the back. The dog has a distinctive coat pattern, featuring a white face, brown and black fur, and white paws, and is standing on a skateboard with red wheels, set against a blurred background of a street or alleyway with a teal door and beige wall.<|eot|>
The image depicts a dog standing on a skateboard, positioned centrally and facing the camera directly. The dog has a distinctive coat pattern featuring white, black, and brown fur, with floppy ears and a black nose, and is standing on a skateboard with red wheels.<|eot|>
```
@ -91,7 +91,7 @@ Here is an example of how to pass an image to the model
##### Model Response Format
```
This image shows a dog standing on a skateboard, with its front paws positioned near the front of the board and its back paws near the back. The dog has a white, black, and orange coat, and is standing on a gray skateboard with red wheels, in front of a blurred background that appears to be a street or alleyway.<|eot|>
The image depicts a dog standing on a skateboard, with the dog positioned centrally and facing forward. The dog has a distinctive coat featuring a mix of white, brown, and black fur, and is wearing a collar as it stands on the skateboard, which has red wheels.<|eot|>
```
@ -117,7 +117,7 @@ Here is an example of how to pass an image to the model
##### Model Response Format
```
The first image shows a dog standing on a skateboard, while the second image shows a plate of spaghetti with tomato sauce, parmesan cheese, and parsley. The two images are unrelated, with the first image featuring a dog and the second image featuring a food dish, and they do not share any common elements or themes.<|eot|>
The first image features a dog standing on a skateboard, while the second image showcases a plate of spaghetti with tomato sauce and cheese. The two images appear to be unrelated, with one depicting a playful scene of a dog on a skateboard and the other presenting a classic Italian dish.<|eom|>
```
@ -135,13 +135,44 @@ We are continuing the format for zero shot function calling used in previous ver
```
<|begin_of_text|><|header_start|>system<|header_end|>
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.
You are a helpful assistant and an expert in function composition. You can answer general questions using your internal knowledge OR invoke functions when necessary. Follow these strict guidelines:
1. FUNCTION CALLS:
- ONLY use functions that are EXPLICITLY listed in the function list below
- If NO functions are listed (empty function list []), respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information"
- If a function is not in the list, respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information"
- If ALL required parameters are present AND the query EXACTLY matches a listed function's purpose: output ONLY the function call(s)
- Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)]
Examples:
CORRECT: [get_weather(location="Vancouver"), calculate_route(start="Boston", end="New York")] <- Only if get_weather and calculate_route are in function list
INCORRECT: get_weather(location="New York")
INCORRECT: Let me check the weather: [get_weather(location="New York")]
INCORRECT: [get_events(location="Singapore")] <- If function not in list
2. RESPONSE RULES:
- For pure function requests matching a listed function: ONLY output the function call(s)
- For knowledge questions: ONLY output text
- For missing parameters: ONLY request the specific missing parameters
- For unavailable services (not in function list): output ONLY with internal knowledge or "I don't have access to [Unavailable service] information". Do NOT execute a function call.
- If the query asks for information beyond what a listed function provides: output ONLY with internal knowledge about your limitations
- NEVER combine text and function calls in the same response
- NEVER suggest alternative functions when the requested service is unavailable
- NEVER create or invent new functions not listed below
3. STRICT BOUNDARIES:
- ONLY use functions from the list below - no exceptions
- NEVER use a function as an alternative to unavailable information
- NEVER call functions not present in the function list
- NEVER add explanatory text to function calls
- NEVER respond with empty brackets
- Use proper Python/JSON syntax for function calls
- Check the function list carefully before responding
4. TOOL RESPONSE HANDLING:
- When receiving tool responses: provide concise, natural language responses
- Don't repeat tool response verbatim
- Don't add supplementary information
If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke.
@ -151,9 +182,7 @@ Here is a list of functions in JSON format that you can invoke.
"description": "Get weather info for places",
"parameters": {
"type": "dict",
"required": [
"city"
],
"required": ["city"],
"properties": {
"city": {
"type": "string",
@ -167,7 +196,10 @@ Here is a list of functions in JSON format that you can invoke.
}
}
}
<|eot|><|header_start|>user<|header_end|>
]
You can answer general questions or invoke tools when necessary.
In addition to tool calls, you should also augment your responses by using the tool outputs.<|eot|><|header_start|>user<|header_end|>
What is the weather in SF and Seattle?<|eot|><|header_start|>assistant<|header_end|>
@ -176,7 +208,7 @@ What is the weather in SF and Seattle?<|eot|><|header_start|>assistant<|header_e
##### Model Response Format
```
[get_weather(city='SF'), get_weather(city='Seattle')]<|eot|>
[get_weather(city="San Francisco"), get_weather(city="Seattle")]<|eot|>
```
@ -273,5 +305,5 @@ Use tools to get latest trending songs<|eot|><|header_start|>assistant<|header_e
##### Model Response Format
```
<function=trending_songs>{"n": "10"}</function><|eot|>
<function=trending_songs>{"n": 10}</function><|eot|>
```

40
llama_stack/models/llama/llama4/prompts.py
View file

@ -9,6 +9,10 @@ from io import BytesIO
from pathlib import Path
from typing import List
from llama_stack.models.llama.llama4.prompt_templates.system_prompts import (
PythonListCustomToolGenerator,
)
from ..datatypes import RawMediaItem, RawMessage, RawTextItem
from ..prompt_format import (
Llama4UseCase,
@ -177,39 +181,9 @@ def usecases(base_model: bool = False) -> List[UseCase | str]:
[
RawMessage(
role="system",
content="""You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.
If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke.
[
{
"name": "get_weather",
"description": "Get weather info for places",
"parameters": {
"type": "dict",
"required": [
"city"
],
"properties": {
"city": {
"type": "string",
"description": "The name of the city to get the weather for"
},
"metric": {
"type": "string",
"description": "The metric for weather. Options are: celsius, fahrenheit",
"default": "celsius"
}
}
}
}
""",
content=PythonListCustomToolGenerator()
.gen(PythonListCustomToolGenerator().data_examples()[0])
.render(),
),
RawMessage(
role="user",

37
llama_stack/providers/remote/post_training/nvidia/post_training.py
View file

@ -67,13 +67,18 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
self.timeout = aiohttp.ClientTimeout(total=config.timeout)
# TODO: filter by available models based on /config endpoint
ModelRegistryHelper.__init__(self, model_entries=_MODEL_ENTRIES)
self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout)
self.customizer_url = config.customizer_url
self.session = None
self.customizer_url = config.customizer_url
if not self.customizer_url:
warnings.warn("Customizer URL is not set, using default value: http://nemo.test", stacklevel=2)
self.customizer_url = "http://nemo.test"
async def _get_session(self) -> aiohttp.ClientSession:
if self.session is None or self.session.closed:
self.session = aiohttp.ClientSession(headers=self.headers, timeout=self.timeout)
return self.session
async def _make_request(
self,
method: str,
@ -94,8 +99,9 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
if json and "Content-Type" not in request_headers:
request_headers["Content-Type"] = "application/json"
session = await self._get_session()
for _ in range(self.config.max_retries):
async with self.session.request(method, url, params=params, json=json, **kwargs) as response:
async with session.request(method, url, params=params, json=json, **kwargs) as response:
if response.status >= 400:
error_data = await response.json()
raise Exception(f"API request failed: {error_data}")
@ -122,8 +128,8 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
jobs = []
for job in response.get("data", []):
job_id = job.pop("id")
job_status = job.pop("status", "unknown").lower()
mapped_status = STATUS_MAPPING.get(job_status, "unknown")
job_status = job.pop("status", "scheduled").lower()
mapped_status = STATUS_MAPPING.get(job_status, "scheduled")
# Convert string timestamps to datetime objects
created_at = (
@ -177,7 +183,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
)
api_status = response.pop("status").lower()
mapped_status = STATUS_MAPPING.get(api_status, "unknown")
mapped_status = STATUS_MAPPING.get(api_status, "scheduled")
return NvidiaPostTrainingJobStatusResponse(
status=JobStatus(mapped_status),
@ -239,6 +245,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
Supported models:
- meta/llama-3.1-8b-instruct
- meta/llama-3.2-1b-instruct
Supported algorithm configs:
- LoRA, SFT
@ -284,10 +291,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
- LoRA config:
## NeMo customizer specific LoRA parameters
- adapter_dim: int - Adapter dimension
Default: 8 (supports powers of 2)
- adapter_dropout: float - Adapter dropout
Default: None (0.0-1.0)
- alpha: int - Scaling factor for the LoRA update
Default: 16
Note:
@ -297,7 +300,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
User is informed about unsupported parameters via warnings.
"""
# Map model to nvidia model name
# ToDo: only supports llama-3.1-8b-instruct now, need to update this to support other models
# See `_MODEL_ENTRIES` for supported models
nvidia_model = self.get_provider_model_id(model)
# Check for unsupported method parameters
@ -330,7 +333,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
},
"data_config": {"dataset_id", "batch_size"},
"optimizer_config": {"lr", "weight_decay"},
"lora_config": {"type", "adapter_dim", "adapter_dropout", "alpha"},
"lora_config": {"type", "alpha"},
}
# Validate all parameters at once
@ -389,16 +392,10 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
# Handle LoRA-specific configuration
if algorithm_config:
if isinstance(algorithm_config, dict) and algorithm_config.get("type") == "LoRA":
if algorithm_config.type == "LoRA":
warn_unsupported_params(algorithm_config, supported_params["lora_config"], "LoRA config")
job_config["hyperparameters"]["lora"] = {
k: v
for k, v in {
"adapter_dim": algorithm_config.get("adapter_dim"),
"alpha": algorithm_config.get("alpha"),
"adapter_dropout": algorithm_config.get("adapter_dropout"),
}.items()
if v is not None
k: v for k, v in {"alpha": algorithm_config.alpha}.items() if v is not None
}
else:
raise NotImplementedError(f"Unsupported algorithm config: {algorithm_config}")

2
llama_stack/templates/meta-reference-gpu/doc_template.md
View file

@ -69,6 +69,7 @@ LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
--gpu all \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
@ -82,6 +83,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
docker run \
-it \
--pull always \
--gpu all \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \