From 0c18809cd6259ee5982a56f3007f83048bb58d27 Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Mon, 13 May 2024 18:35:14 +0200 Subject: [PATCH 01/20] Add files via upload --- Function_Calling.md | 354 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 354 insertions(+) create mode 100644 Function_Calling.md diff --git a/Function_Calling.md b/Function_Calling.md new file mode 100644 index 0000000000..db357dea9a --- /dev/null +++ b/Function_Calling.md @@ -0,0 +1,354 @@ +# Function Calling in Hugging Face is here ! + +## Introduction + +A few weeks ago, we introduced to you the brand new [Messages AP](https://huggingface.co/blog/tgi-messages-api)I that provided OpenAI compatibility with Text Generation Inference (TGI) and Inference Endpoints. + +We wrote at the time that “*The Messages API does not currently support function calling” this is a limitation that has now been lifted !* + +Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change as been propagated in the**`huggingface_hub`** version **0.23.0.** Meaning any Huggingface endpoint can now call some tools if using a newer version than those. + +This new feature is available in Inference Endpoints (dedicated and serverless). and we’ll showcase how you can start building your open-source agents right away. + +To get you started quickly, we’ve included detailed code examples of how to: + +- Create an Inference Endpoint +- Call Function with the InferencClient +- Use OpenAI’s SDK +- Integration with LangChain and LlamaIndex + +## **Create an Inference Endpoint using `huggingface_hub`** + +[Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) offers a secure, production solution to easily deploy any Transformers model from the Hub on dedicated infrastructure managed by Hugging Face. + +To showcase this newfound power of TGI, we will deploy a 8B instruct tuned model : + +[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) + +We can deploy the model in just [a few clicks from the UI](https://ui.endpoints.huggingface.co/new?vendor=aws&repository=NousResearch%2FNous-Hermes-2-Mixtral-8x7B-DPO&tgi_max_total_tokens=32000&tgi=true&tgi_max_input_length=1024&task=text-generation&instance_size=2xlarge&tgi_max_batch_prefill_tokens=2048&tgi_max_batch_total_tokens=1024000&no_suggested_compute=true&accelerator=gpu®ion=us-east-1), or take advantage of the `huggingface_hub` Python library to programmatically create and manage Inference Endpoints. We demonstrate the use of the Hub library below. + +First, we need to specify the endpoint name and model repository, along with the task of text-generation. A protected Inference Endpoint means a valid HF token is required to access the deployed API. We also need to configure the hardware requirements like vendor, region, accelerator, instance type, and size. You can check out the list of available resource options [here](https://api.endpoints.huggingface.cloud/#get-/v2/provider) and view recommended configurations for select models in our catalog [here](https://ui.endpoints.huggingface.co/catalog). + +```python +from huggingface_hub import create_inference_endpoint + +endpoint = create_inference_endpoint( + "llama-3-8b-function-calling", + repository="meta-llama/Meta-Llama-3-8B-Instruct", + framework="pytorch", + task="text-generation", + accelerator="gpu", + vendor="aws", + region="us-east-1", + type="protected", + instance_type="nvidia-a10g", + instance_size="x1", + custom_image={ + "health_route": "/health", + "env": { + "MAX_INPUT_LENGTH": "3500", + "MAX_BATCH_PREFILL_TOKENS": "3500", + "MAX_TOTAL_TOKENS": "4096", + "MAX_BATCH_TOTAL_TOKENS": "4096", + "HUGGING_FACE_HUB_TOKEN":"", + "MODEL_ID": "/repository", + }, + "url": "ghcr.io/huggingface/text-generation-inference:latest", # use this build or newer + }, +) + +endpoint.wait() +print(endpoint.status) + +``` + +Since the model is gated, it will be very important to replace `` with your own Huggingface token once you have accepted the terms and condition of Llama-3-8B-Instruct on the [model page](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) + +It will take a few minutes for our deployment to spin up. We can utilize the `.wait()` utility to block the running thread until the endpoint reaches a final "running" state. Once running, we can confirm its status and take it for a spin via the UI Playground: + +[ENDPOINT IMAGE] + +Great, we now have a working deployment! + + + +## Using Inference Endpoints via OpenAI client libraries + +The added support for messages in TGI makes Inference Endpoints directly compatible with the OpenAI Chat Completion API. This means that any existing scripts that use OpenAI models via the OpenAI client libraries can be directly swapped out to use any open LLM running on a TGI endpoint! + +With this seamless transition, you can immediately take advantage of the numerous benefits offered by open models: + +- Complete control and transparency over models and data +- No more worrying about rate limits +- The ability to fully customize systems according to your specific needs + +Lets see how. + +### With the InferencClient from Hugging Face + +Function can directly be called with the serverless API, or with any endpoint by with the enpoint url. + +```jsx +from huggingface_hub import InferenceClient + +# Ask for weather in the next days using tools +#client = InferenceClient("") +#or +client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") +messages = [ + {"role": "system", "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous."}, + {"role": "user", "content": "What's the weather like in Paris, France?"}, +] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], + }, + }, + }, + + ] +response = client.chat_completion( + model="meta-llama/Meta-Llama-3-70B-Instruct", + messages=messages, + tools=tools, + tool_choice="auto", + max_tokens=500, + ) +response.choices[0].message.tool_calls[0].function +``` + +```python +ChatCompletionOutputFunctionDefinition(arguments={'format': 'celsius', 'location': 'Paris, France'}, name='get_current_weather', description=None) +``` + +### With the OpenAI Python client + +The example below shows how to make this transition using the [OpenAI Python Library](https://github.com/openai/openai-python). Simply replace the `` with your endpoint URL (be sure to include the `v1/` the suffix) and populate the `` field with a valid Hugging Face user token. + +We can then use the client as usual, passing a list of messages to stream responses from our Inference Endpoint. + +```python +from openai import OpenAI + +# initialize the client but point it to TGI +client = OpenAI( + base_url="" + "/v1/", # replace with your endpoint url + api_key="", # replace with your token +) + +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], + }, + }, + } +] +chat_completion = client.chat.completions.create( + model="tgi", + messages=[ + { + "role": "user", + "content": "What's the weather like in Celsius in San Francisco, CA?", + }, + ], + tools=tools, + tool_choice="auto", # tool selected by caller + max_tokens=500, +) + +called = chat_completion.choices[0] +print(called) +``` + +```python +Choice(finish_reason='eos_token', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id=0, function=Function(arguments={'format': 'celsius', 'location': 'San Francisco, CA'}, name='get_current_weather', description=None), type='function')])) +``` + +Behind the scenes, TGI’s Messages API automatically converts the list of messages into the model’s required instruction format using it’s [chat template](https://huggingface.co/docs/transformers/chat_templating). You can learn more about chat templates on the [documentation](https://huggingface.co/docs/transformers/main/en/chat_templating) or on this [space](https://huggingface.co/spaces/Jofthomas/Chat_template_viewer) ! + + + +## How to use with LangChain + +Now, let’s see how to use functions in the newly created package `langchain_huggingface` + +```python +from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser + +llm = HuggingFaceEndpoint( + endpoint_url="https://aac2dhzj35gskpof.us-east-1.aws.endpoints.huggingface.cloud", + task="text-generation", + max_new_tokens=1024, + do_sample=False, + repetition_penalty=1.03, +) +llm_engine_hf = ChatHuggingFace(llm=llm) + +class calculator(BaseModel): + """Multiply two integers together.""" + a: int = Field(..., description="First integer") + b: int = Field(..., description="Second integer") + +llm_with_multiply = llm_engine_hf.bind_tools([calculator], tool_choice="auto") +tool_chain = llm_with_multiply +tool_chain.invoke("what's 3 * 12") +``` + +```python +AIMessage(content='', additional_kwargs={'tool_calls': [ChatCompletionOutputToolCall(function=ChatCompletionOutputFunctionDefinition(arguments={'a': 3, 'b': 12}, name='calculator', description=None), id=0, type='function')]}, response_metadata={'token_usage': ChatCompletionOutputUsage(completion_tokens=23, prompt_tokens=154, total_tokens=177), 'model': '', 'finish_reason': 'eos_token'}, id='run-cb823ae4-665e-4c88-b1c6-e69ae5cbbc74-0', tool_calls=[{'name': 'calculator', 'args': {'a': 3, 'b': 12}, 'id': 0}])We’re able to directly leverage the same`ChatOpenAI` class that we would have used with the OpenAI models. This allows all previous code to function with our endpoint by changing just one line of code. +Let’s now use this declared LLM in a simple RAG pipeline to answer a question over the contents of a HF blog post. +``` + +## How to use with LlamaIndex + +Similarly, you can also use a tools with TGI endpoints in [LLamaIndex](https://www.llamaindex.ai/), but not the serverless API yet + +```python +import os +from typing import List, Literal,Optional +from llama_index.core.bridge.pydantic import BaseModel, Field +from llama_index.core.tools import FunctionTool +from llama_index.core.base.llms.types import ( + ChatMessage, + MessageRole, +) + +from llama_index.llms.huggingface import ( + TextGenerationInference, +) + +URL = "your_tgi_endpoint" +model = TextGenerationInference( + model_url=URL, token=False +) # set token to False in case of public endpoint + +def get_current_weather(location: str, format: str): + """Get the current weather + + Args: + location (str): The city and state, e.g. San Francisco, CA + format (str): The temperature unit to use ('celsius' or 'fahrenheit'). Infer this from the users location. + """ + ... + +class WeatherArgs(BaseModel): + location: str = Field( + description="The city and region, e.g. Paris, Ile-de-France" + ) + format: Literal["fahrenheit", "celsius"] = Field( + description="The temperature unit to use ('fahrenheit' or 'celsius'). Infer this from the location.", + ) + +weather_tool = FunctionTool.from_defaults( + fn=get_current_weather, + name="get_current_weather", + description="Get the current weather", + fn_schema=WeatherArgs, +) + +def get_current_weather_n_days(location: str, format: str, num_days: int): + """Get the weather forecast for the next N days + + Args: + location (str): The city and state, e.g. San Francisco, CA + format (str): The temperature unit to use ('celsius' or 'fahrenheit'). Infer this from the users location. + num_days (int): The number of days for the weather forecast. + """ + ... + +class ForecastArgs(BaseModel): + location: str = Field( + description="The city and region, e.g. Paris, Ile-de-France" + ) + format: Literal["fahrenheit", "celsius"] = Field( + description="The temperature unit to use ('fahrenheit' or 'celsius'). Infer this from the location.", + ) + num_days: int = Field( + description="The duration for the weather forecast in days.", + ) + +forecast_tool = FunctionTool.from_defaults( + fn=get_current_weather_n_days, + name="get_current_weather_n_days", + description="Get the current weather for n days", + fn_schema=ForecastArgs, +) + +usr_msg = ChatMessage( + role=MessageRole.USER, + content="What's the weather like in Paris over next week?", +) + +response = model.chat_with_tools( + user_msg=usr_msg, + tools=[ + weather_tool, + forecast_tool, + ], + tool_choice="get_current_weather_n_days", +) + +print(response.message.additional_kwargs) +``` + +```python + +{'tool_calls': [{'id': 0, 'type': 'function', 'function': {'description': None, 'name': 'get_current_weather_n_days', 'arguments': {'format': 'celsius', 'location': 'Paris, Ile-de-France', 'num_days': 7}}}]} +``` + +## Clean up + +To clean up our work, we can either pause or delete the model endpoint. This step can alternately be completed via the UI. + +```python +# pause our running endpoint +endpoint.pause() + +# optionally delete +endpoint.delete() +``` + +## Conclusion + +You can now call some tools with Hugging Face models in the different frameworks. 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++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/Function_Calling.md b/Function_Calling.md index db357dea9a..f08b3c4f3b 100644 --- a/Function_Calling.md +++ b/Function_Calling.md @@ -1,3 +1,12 @@ +--- +title: "Function calling with Hugging Face" +thumbnail: /blog/assets/Function_call/Thumbnail.png +authors: +- user: jofthomas +- user: drbh +- user: kkondratenko + guest: true +--- # Function Calling in Hugging Face is here ! ## Introduction @@ -351,4 +360,4 @@ endpoint.delete() ## Conclusion -You can now call some tools with Hugging Face models in the different frameworks. We can’t wait to see what use cases you will power with open LLMs and tools ! \ No newline at end of file +You can now call some tools with Hugging Face models in the different frameworks. We can’t wait to see what use cases you will power with open LLMs and tools ! 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zeVC2Fc=x$Ys6t>J)++1cVdY{^hmdC;?B~nt-2#2FM%geE!P1855-oXb?h~Y94N0XE ztt5}YbfILg3E|ZQnpj6&ReGSmgcDHf?%2z6oNH>GM<45Mh{%JvD?5Hi2QV(+>p(zh z^XIR>@h#`!^dQcZ(oI@^X^-3)8v(i${nEiRZq#+rUCmA|5jJ~+vTHJ$ew&ie)uSUosV!Or&wE@;DMLfE(alh z{{fPaV*`fw4`q+tuD*zm`&7V&cVl35xE9Wb^Y|F2=f`}qlX3P=#VaI#oGl<-W8~8# zH(ZG7AqIUTM2bfJzMDoHbJVccgAg#>0W<(&2E~v>yygzcGb}7@@C(3Yxe&Y2&k~QA zIPE@YU&_nIc3gaDg^i8B73);58x25Rd7@})n?s!b{_|XPLhrZFb3(rk*_molNy-u6 z-t_&reN;y8tBi4Yjpm!4Vpc*+dZvqniOq{?IZ`N_=3XFnG_RF5SZP!K&xaA#)4TxxS!Z@dTCX-C=E z`c(d%5t#INLCE#CGAc5TDVFiLDMuL|WwQUR*Q|6I2&GlWJ9c#LfQ>JlXO=;Yw*d`} zhN1AufGKqU=CpiC#zf|X^wuL2OXoq!liBh85aDE@F#h>pDE~W5nf^F&VYO+QIAOK5 zY1SAe%zgX~Ir{M(%iyQ1=V7P*cir#vZv=iL@Ed{uM+9uX|2(2Eyv%+SpU(4Z{gx2h KD|KeK6aE8+q6VM< From 8a5ccec9947f32bda8563923d8ead31207fc30eb Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Mon, 13 May 2024 22:31:47 +0200 Subject: [PATCH 06/20] Edit thumbnail path --- Function_Calling.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Function_Calling.md b/Function_Calling.md index f08b3c4f3b..d0897ee2e9 100644 --- a/Function_Calling.md +++ b/Function_Calling.md @@ -1,6 +1,6 @@ --- title: "Function calling with Hugging Face" -thumbnail: /blog/assets/Function_call/Thumbnail.png +thumbnail: /blog/assets/Function_call/thumbnail.png authors: - user: jofthomas - user: drbh From 38e4af546955b37659d387a006e30c3cf52d1511 Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Tue, 14 May 2024 01:42:43 +0200 Subject: [PATCH 07/20] Update and rename Function_Calling.md to tool_calling.md --- Function_Calling.md => tool_calling.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) rename Function_Calling.md => tool_calling.md (93%) diff --git a/Function_Calling.md b/tool_calling.md similarity index 93% rename from Function_Calling.md rename to tool_calling.md index d0897ee2e9..ab3d1a6c78 100644 --- a/Function_Calling.md +++ b/tool_calling.md @@ -1,28 +1,28 @@ --- -title: "Function calling with Hugging Face" -thumbnail: /blog/assets/Function_call/thumbnail.png +title: "Tool calling with Hugging Face" +thumbnail: /blog/assets/c authors: - user: jofthomas - user: drbh - user: kkondratenko guest: true --- -# Function Calling in Hugging Face is here ! +# Tool Calling in Hugging Face is here ! ## Introduction -A few weeks ago, we introduced to you the brand new [Messages AP](https://huggingface.co/blog/tgi-messages-api)I that provided OpenAI compatibility with Text Generation Inference (TGI) and Inference Endpoints. +A few weeks ago, we introduced to you the brand new [Messages API](https://huggingface.co/blog/tgi-messages-api) that provided OpenAI compatibility with Text Generation Inference (TGI) and Inference Endpoints. We wrote at the time that “*The Messages API does not currently support function calling” this is a limitation that has now been lifted !* -Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change as been propagated in the**`huggingface_hub`** version **0.23.0.** Meaning any Huggingface endpoint can now call some tools if using a newer version than those. +Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change as been propagated in the**`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version This new feature is available in Inference Endpoints (dedicated and serverless). and we’ll showcase how you can start building your open-source agents right away. To get you started quickly, we’ve included detailed code examples of how to: - Create an Inference Endpoint -- Call Function with the InferencClient +- Call tools with the InferenClient - Use OpenAI’s SDK - Integration with LangChain and LlamaIndex @@ -98,7 +98,7 @@ Lets see how. ### With the InferencClient from Hugging Face -Function can directly be called with the serverless API, or with any endpoint by with the enpoint url. +Function can directly be called with the serverless API, or with any endpoint by with the endpoint url. ```jsx from huggingface_hub import InferenceClient @@ -224,6 +224,8 @@ Now, let’s see how to use functions in the newly created package `langchain_hu ```python from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser +from langchain_huggingface.llms import HuggingFaceEndpoint +from langchain_huggingface.chat_models.huggingface import ChatHuggingFace llm = HuggingFaceEndpoint( endpoint_url="https://aac2dhzj35gskpof.us-east-1.aws.endpoints.huggingface.cloud", @@ -360,4 +362,4 @@ endpoint.delete() ## Conclusion -You can now call some tools with Hugging Face models in the different frameworks. We can’t wait to see what use cases you will power with open LLMs and tools ! +Now that you can now call some tools with Hugging Face models in the different frameworks, we strongly encourage you to deploy ( and possibly fine tune ) your own models in an Inference Endpoint and experiment with this new feature. We are convinced that the capacity of small LLMs to call some tools will be very beneficial to the community. We can’t wait to see what use cases you will power with open LLMs and tools ! 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z#&4&|@=yIMpX=EvS=kGrX2K13<9+>sWgolk!SoL@d#`OV>9RR)b9s|n)3(d}kvrC= o&n)+1kF;z0q+5Is6i0f0>fd_$zopr05Q;8(*OVf literal 0 HcmV?d00001 From 99fc14c34f18d2e66b3bdc856922c58e4759a3a3 Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Wed, 15 May 2024 00:24:58 +0200 Subject: [PATCH 11/20] Update _blog.yml --- _blog.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/_blog.yml b/_blog.yml index 84f08730bc..d1be78c247 100644 --- a/_blog.yml +++ b/_blog.yml @@ -3972,3 +3972,16 @@ - llama3 - langchain - benchmark + +- local: tool_calling + title: "Tool calling with Hugging Face" + thumbnail: /blog/assets/tool_calling/thumbnail.png + author: jofthomas + date: May 15, 2024 + tags: + - nlp + - LLM + - agents + - inference + - guide + From a9f23408d93b702945cbe2cf6be6bfcd5963b659 Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Wed, 15 May 2024 00:31:10 +0200 Subject: [PATCH 12/20] Apply suggestions from omar's review Co-authored-by: Omar Sanseviero --- tool_calling.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/tool_calling.md b/tool_calling.md index 2a7bcec0d2..94e1238e0d 100644 --- a/tool_calling.md +++ b/tool_calling.md @@ -7,34 +7,34 @@ authors: - user: kkondratenko guest: true --- -# Tool Calling in Hugging Face is here ! +# Tool Calling in Hugging Face is here! ## Introduction -A few weeks ago, we introduced to you the brand new [Messages API](https://huggingface.co/blog/tgi-messages-api) that provided OpenAI compatibility with Text Generation Inference (TGI) and Inference Endpoints. +A few weeks ago, we introduced the new [Messages API](https://huggingface.co/blog/tgi-messages-api) that provided OpenAI compatibility with Text Generation Inference (TGI) and Inference Endpoints. -We wrote at the time that “*The Messages API does not currently support function calling” this is a limitation that has now been lifted !* +At the time, the Messages API did not support function calling. This is a limitation that has now been lifted ! -Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change as been propagated in the**`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version +Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change has been propagated in the **`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version -This new feature is available in Inference Endpoints (dedicated and serverless). and we’ll showcase how you can start building your open-source agents right away. +This new feature is available in Inference Endpoints (dedicated and serverless). We’ll now showcase how you can start building your open-source agents right away. To get you started quickly, we’ve included detailed code examples of how to: - Create an Inference Endpoint - Call tools with the InferenClient - Use OpenAI’s SDK -- Integration with LangChain and LlamaIndex +- Leverage LangChain and LlamaIndex integrations ## **Create an Inference Endpoint using `huggingface_hub`** [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) offers a secure, production solution to easily deploy any Transformers model from the Hub on dedicated infrastructure managed by Hugging Face. -To showcase this newfound power of TGI, we will deploy a 8B instruct tuned model : +To showcase this newfound power of TGI, we will deploy an 8B instruct tuned model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) -We can deploy the model in just [a few clicks from the UI](https://ui.endpoints.huggingface.co/new?vendor=aws&repository=NousResearch%2FNous-Hermes-2-Mixtral-8x7B-DPO&tgi_max_total_tokens=32000&tgi=true&tgi_max_input_length=1024&task=text-generation&instance_size=2xlarge&tgi_max_batch_prefill_tokens=2048&tgi_max_batch_total_tokens=1024000&no_suggested_compute=true&accelerator=gpu®ion=us-east-1), or take advantage of the `huggingface_hub` Python library to programmatically create and manage Inference Endpoints. We demonstrate the use of the Hub library below. +We can deploy the model in just [a few clicks from the UI](https://ui.endpoints.huggingface.co/new?vendor=aws&repository=NousResearch%2FNous-Hermes-2-Mixtral-8x7B-DPO&tgi_max_total_tokens=32000&tgi=true&tgi_max_input_length=1024&task=text-generation&instance_size=2xlarge&tgi_max_batch_prefill_tokens=2048&tgi_max_batch_total_tokens=1024000&no_suggested_compute=true&accelerator=gpu®ion=us-east-1) or take advantage of the `huggingface_hub` Python library to programmatically create and manage Inference Endpoints. We demonstrate the use of the Hub library below. First, we need to specify the endpoint name and model repository, along with the task of text-generation. A protected Inference Endpoint means a valid HF token is required to access the deployed API. We also need to configure the hardware requirements like vendor, region, accelerator, instance type, and size. You can check out the list of available resource options [here](https://api.endpoints.huggingface.cloud/#get-/v2/provider) and view recommended configurations for select models in our catalog [here](https://ui.endpoints.huggingface.co/catalog). @@ -71,7 +71,7 @@ print(endpoint.status) ``` -Since the model is gated, it will be very important to replace `` with your own Huggingface token once you have accepted the terms and condition of Llama-3-8B-Instruct on the [model page](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) +Since the model is gated, it will be very important to replace `` with your own Hugging Face token once you have accepted the terms and conditions of Llama-3-8B-Instruct on the [model page](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) It will take a few minutes for our deployment to spin up. We can utilize the `.wait()` utility to block the running thread until the endpoint reaches a final "running" state. Once running, we can confirm its status and take it for a spin via the UI Playground: @@ -94,13 +94,13 @@ With this seamless transition, you can immediately take advantage of the numerou - No more worrying about rate limits - The ability to fully customize systems according to your specific needs -Lets see how. +Let's see how. ### With the InferencClient from Hugging Face -Function can directly be called with the serverless API, or with any endpoint by with the endpoint url. +The function can directly be called with the serverless API or with any endpoint by with the endpoint URL. -```jsx +```py from huggingface_hub import InferenceClient # Ask for weather in the next days using tools @@ -152,7 +152,7 @@ ChatCompletionOutputFunctionDefinition(arguments={'format': 'celsius', 'location ### With the OpenAI Python client -The example below shows how to make this transition using the [OpenAI Python Library](https://github.com/openai/openai-python). Simply replace the `` with your endpoint URL (be sure to include the `v1/` the suffix) and populate the `` field with a valid Hugging Face user token. +The example below shows how to make this transition using the [OpenAI Python Library](https://github.com/openai/openai-python). Simply replace the `` with your endpoint URL (be sure to include the `v1/` suffix) and populate the `` field with a valid Hugging Face user token. We can then use the client as usual, passing a list of messages to stream responses from our Inference Endpoint. @@ -210,10 +210,10 @@ print(called) Choice(finish_reason='eos_token', index=0, logprobs=None, message=ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id=0, function=Function(arguments={'format': 'celsius', 'location': 'San Francisco, CA'}, name='get_current_weather', description=None), type='function')])) ``` -Behind the scenes, TGI’s Messages API automatically converts the list of messages into the model’s required instruction format using it’s [chat template](https://huggingface.co/docs/transformers/chat_templating). You can learn more about chat templates on the [documentation](https://huggingface.co/docs/transformers/main/en/chat_templating) or on this [space](https://huggingface.co/spaces/Jofthomas/Chat_template_viewer) ! +Behind the scenes, TGI’s Messages API automatically converts the list of messages into the model’s required instruction format using it’s [chat template](https://huggingface.co/docs/transformers/chat_templating). You can learn more about chat templates on the [documentation](https://huggingface.co/docs/transformers/main/en/chat_templating) or on this [space](https://huggingface.co/spaces/Jofthomas/Chat_template_viewer)!

@@ -253,7 +253,7 @@ Let’s now use this declared LLM in a simple RAG pipeline to answer a question ## How to use with LlamaIndex -Similarly, you can also use a tools with TGI endpoints in [LLamaIndex](https://www.llamaindex.ai/), but not the serverless API yet +Similarly, you can also use a tool with TGI endpoints in [LLamaIndex](https://www.llamaindex.ai/), but not the serverless API yet ```python import os @@ -362,4 +362,4 @@ endpoint.delete() ## Conclusion -Now that you can now call some tools with Hugging Face models in the different frameworks, we strongly encourage you to deploy ( and possibly fine tune ) your own models in an Inference Endpoint and experiment with this new feature. We are convinced that the capacity of small LLMs to call some tools will be very beneficial to the community. We can’t wait to see what use cases you will power with open LLMs and tools ! +Now that you can call some tools with Hugging Face models in the different frameworks, we strongly encourage you to deploy ( and possibly fine-tune) your own models in an Inference Endpoint and experiment with this new feature. We are convinced that the capacity of small LLMs to call some tools will be very beneficial to the community. We can’t wait to see what use cases you will power with open LLMs and tools! From 830cf964976ff120366e09788d3c96d8c00ca62b Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Wed, 15 May 2024 01:10:11 +0200 Subject: [PATCH 13/20] update aside tag that is not working --- tool_calling.md | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/tool_calling.md b/tool_calling.md index 94e1238e0d..3bf2aa2afb 100644 --- a/tool_calling.md +++ b/tool_calling.md @@ -15,7 +15,7 @@ A few weeks ago, we introduced the new [Messages API](https://huggingface.co/blo At the time, the Messages API did not support function calling. This is a limitation that has now been lifted ! -Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change has been propagated in the **`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version +Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change has been propagated in the **`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version. This new feature is available in Inference Endpoints (dedicated and serverless). We’ll now showcase how you can start building your open-source agents right away. @@ -79,10 +79,9 @@ It will take a few minutes for our deployment to spin up. We can utilize the `.w Great, we now have a working deployment! - +> ##### 💡 By default, your endpoint will scale-to-zero after 15 minutes of idle time without any requests to optimize cost during periods of inactivity. Check out [the Hub Python Library documentation](https://huggingface.co/docs/huggingface_hub/guides/inference_endpoints) to see all the functionality available for managing your endpoint lifecycle. + ## Using Inference Endpoints via OpenAI client libraries @@ -212,10 +211,8 @@ Choice(finish_reason='eos_token', index=0, logprobs=None, message=ChatCompletion Behind the scenes, TGI’s Messages API automatically converts the list of messages into the model’s required instruction format using it’s [chat template](https://huggingface.co/docs/transformers/chat_templating). You can learn more about chat templates on the [documentation](https://huggingface.co/docs/transformers/main/en/chat_templating) or on this [space](https://huggingface.co/spaces/Jofthomas/Chat_template_viewer)! - ## How to use with LangChain From 5333bf9e8a0672bd349f160be71fb3d0aea476d9 Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Wed, 15 May 2024 01:16:46 +0200 Subject: [PATCH 14/20] remove unused imports --- tool_calling.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/tool_calling.md b/tool_calling.md index 3bf2aa2afb..d9cdc4baf0 100644 --- a/tool_calling.md +++ b/tool_calling.md @@ -219,8 +219,6 @@ Behind the scenes, TGI’s Messages API automatically converts the list of messa Now, let’s see how to use functions in the newly created package `langchain_huggingface` ```python -from langchain_core.pydantic_v1 import BaseModel, Field -from langchain_core.output_parsers.openai_tools import JsonOutputToolsParser from 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This is a limitation that has now been lifted ! +At the time, the Messages API did not support function calling. This is a limitation that has now been lifted! Starting with version **1.4.5,** TGI offers an API compatible with the OpenAI Chat Completion API with the addition of the `tools` and the `tools_choice` keys. This change has been propagated in the **`huggingface_hub`** version **0.23.0**, meaning any Hugging Face endpoint can now call some tools if using a newer version. @@ -95,7 +95,7 @@ With this seamless transition, you can immediately take advantage of the numerou Let's see how. -### With the InferencClient from Hugging Face +### With the InferenceClient from Hugging Face The function can directly be called with the serverless API or with any endpoint by with the endpoint URL. From c79532d899147336eaf2f9524bce17b0ef06d6da Mon Sep 17 00:00:00 2001 From: Jofthomas <52778543+Jofthomas@users.noreply.github.com> Date: Wed, 15 May 2024 14:28:00 +0200 Subject: [PATCH 20/20] add image --- tool_calling.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tool_calling.md b/tool_calling.md index 5350e61ad1..9c990bbf32 100644 --- a/tool_calling.md +++ b/tool_calling.md @@ -75,7 +75,7 @@ Since the model is gated, it will be very important to replace `` with It will take a few minutes for our deployment to spin up. We can utilize the `.wait()` utility to block the running thread until the endpoint reaches a final "running" state. Once running, we can confirm its status and take it for a spin via the UI Playground: -[ENDPOINT IMAGE] +![IE UI Overview](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/tool_calling/endpoint.png) Great, we now have a working deployment!

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