i want a fast free model from qa chatbot from huggingf...

i want a fast free model from qa chatbot from huggingfacellm that gives a response in seconds

13 comments

Please stand by @Ftm as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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To create a fast and free QA chatbot using HuggingFaceLLM, you can use the LlamaIndex library. Here's a basic example of how to set up a HuggingFaceLLM:

Plain Text

import torch
from llama_index.llms import HuggingFaceLLM
from llama_index.prompts.prompts import SimpleInputPrompt

# Define system prompt and query wrapper prompt
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")

# Initialize HuggingFaceLLM
llm = HuggingFaceLLM(
    context_window=4096, 
    max_new_tokens=256,
    generate_kwargs={"temperature": 0.7, "do_sample": False},
    system_prompt=system_prompt,
    query_wrapper_prompt=query_wrapper_prompt,
    tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b",
    model_name="StabilityAI/stablelm-tuned-alpha-3b",
    device_map="auto",
    stopping_ids=[50278, 50279, 50277, 1, 0],
    tokenizer_kwargs={"max_length": 4096},
    # uncomment this if using CUDA to reduce memory usage
    # model_kwargs={"torch_dtype": torch.float16}
)

This example uses the StableLM model from StabilityAI, which is a free and open-source model available on HuggingFace. You can replace the tokenizer_name and model_name with any other model available on HuggingFace.

Once you've set up the LLM, you can use it to generate responses to user queries. For example:

```python

Define a user query

query = "What is the capital of France?"

kkapa.ai

Generate a response

response = llm.complete(query)

Print the response

print(response)

``

Please note that the speed of the response will depend on the complexity of the model and the length of the input and output. Larger models and longer inputs/outputs will take longer to process.

For more information, you can refer to the **LlamaIndex documentation** (<https://gpt-index.readthedocs.io/en/latest/api_reference/llms/huggingface.html>).
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FFtm

@kapa.ai I have already used Stablelm and it was also slow, i want other model from huggingfacellm that is open source and gives fast response

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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If you found the StableLM model to be slow, you might want to try a smaller model. Smaller models generally generate responses faster, but the trade-off is that they might not perform as well on complex tasks.

Unfortunately, the provided extracts do not specify any other HuggingFace models that are both open-source and known to provide faster responses.

However, you can explore the HuggingFace Model Hub () to find a model that suits your needs. You can filter models by task (e.g., text generation), language, and other criteria. Once you've chosen a model, you can use it in your LlamaIndex setup by replacing the model_name and tokenizer_name in the HuggingFaceLLM initialization code.

Remember to consider the model's complexity and the length of the input and output when choosing a model, as these factors can significantly impact response time.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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FFtm

@kapa.ai show me how to make Stablelm answers the users question based on the context and not based on his previous knowledge

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To make StableLM answer the user's question based on the context and not based on its previous knowledge, you can use the HuggingFaceLLM class from the LlamaIndex library. Here's an example of how to set it up:

Plain Text

import torch
from llama_index.llms import HuggingFaceLLM
from llama_index.prompts.prompts import SimpleInputPrompt

# Define system prompt and query wrapper prompt
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")

# Initialize HuggingFaceLLM
llm = HuggingFaceLLM(
    context_window=4096, 
    max_new_tokens=256,
    generate_kwargs={"temperature": 0.7, "do_sample": False},
    system_prompt=system_prompt,
    query_wrapper_prompt=query_wrapper_prompt,
    tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b",
    model_name="StabilityAI/stablelm-tuned-alpha-3b",
    device_map="auto",
    stopping_ids=[50278, 50279, 50277, 1, 0],
    tokenizer_kwargs={"max_length": 4096},
    # uncomment this if using CUDA to reduce memory usage
    # model_kwargs={"torch_dtype": torch.float16}
)

Once you've set up the LLM, you can use it to generate responses to user queries. For example:

Plain Text

# Define a user query
query = "What is the capital of France?"

# Generate a response
response = llm.complete(query)

# Print the response
print(response)

kkapa.ai

This will generate a response based on the context provided in the query, not on any previous knowledge the model might have.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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FFtm

@kapa.ai based on your previous question where should we put the documents to make stablelm in llamaindex answers only from it

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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To make StableLM answer questions based on specific documents, you need to load these documents into LlamaIndex. Here's an example of how to do it:

Plain Text

from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import HuggingFaceLLM

# Load documents
documents = SimpleDirectoryReader("../../data/paul_graham").load_data()

# Initialize HuggingFaceLLM as shown in the previous examples

# ...

# Create a service context
service_context = ServiceContext.from_defaults(chunk_size=512, llm=llm)

# Create an index from the documents
index = VectorStoreIndex.from_documents(documents, service_context=service_context)

In this example, the documents are loaded from a directory named paul_graham located in the data directory at the root of your project. You should replace "../../data/paul_graham" with the path to your documents.

Once you've created the index, you can use it to answer questions based on the loaded documents:

Plain Text

# Create a query engine
query_engine = index.as_query_engine()

# Define a user query
query = "What did the author do growing up?"

# Generate a response
response = query_engine.query(query)

# Print the response
print(response)

This will generate a response based on the documents loaded into the index, not on any previous knowledge the model might have.

For more information, you can refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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i want a fast free model from qa chatbot from huggingfacellm that gives a response in sec

Define a user query

Generate a response

Print the response