how to user local model from modelscorp or huggingface

[LiuChao888]

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Example Code

nnothing

Description

such as tiltle. I have some models in my local. and now I want load my local model by langchain.
that is my question: how to use ?
my local models from modelscop or huggingface.
thanks

System Info

just libs question. no platform

[dosubot[bot]]

Hey @LiuChao888! I'm here to help you with any questions or issues you have. Just let me know how I can assist you!

To load and use a local model from ModelScope or HuggingFace with LangChain, follow these steps:

For HuggingFace Models

1. Install the necessary packages:

   pip install transformers torch

2. Load the model using the transformers library and create a pipeline:

   from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
   from langchain_community.llms import HuggingFacePipeline
   
   model_id = "path_to_your_local_model"  # Replace with your local model path
   tokenizer = AutoTokenizer.from_pretrained(model_id)
   model = AutoModelForCausalLM.from_pretrained(model_id)
   pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10)
   hf = HuggingFacePipeline(pipeline=pipe)

3. Create a chain with a prompt:

   from langchain_core.prompts import PromptTemplate
   
   template = """Question: {question}
   
   Answer: Let's think step by step."""
   prompt = PromptTemplate.from_template(template)
   
   chain = prompt | hf
   
   question = "What is electroencephalography?"
   
   print(chain.invoke({"question": question}))

For ModelScope Models

1. Install the modelscope package:

   pip install modelscope

2. Use the ModelScopeEmbeddings class in your Python code:

   from langchain_community.embeddings import ModelScopeEmbeddings
   
   # Load your local model
   model = ModelScopeEmbeddings(model_id="path_to_your_local_model")
   
   # Use the model for embeddings
   text = "Your text here"
   embeddings = model.embed_query(text)
   print(embeddings)
   
   # Optionally, embed documents
   doc_results = model.embed_documents(["Your document text here"])
   print(doc_results)

Replace "path_to_your_local_model" with the actual path to your local model. This will load the model and allow you to use it for generating embeddings or text generation.

For more detailed instructions, you can refer to the LangChain documentation and the local LLMs setup guide ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[LiuChao888]

thank you for your answer. but I for modelscope . I want use LLM models,not embeddings models.
and then, can you give me a example?
thanks
thanks
thanks

[dosubot[bot]]

To use a local LLM model from ModelScope with LangChain, you can follow this example:

1. Install the necessary packages:

   pip install openllm

2. Load and use the model:

   from langchain_community.llms import OpenLLM
   
   # Initialize the model
   llm = OpenLLM(
       model_name='flan-t5',
       model_id='path_to_your_local_model',  # Replace with your local model path
       embedded=False,
   )
   
   # Invoke the model with a prompt
   response = llm._call("The first man on the moon was ...")
   print(response)

3. Stream tokens as they are being generated (optional):

   from langchain.callbacks.manager import CallbackManager
   from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
   
   # Initialize the model with streaming callback
   llm = OpenLLM(
       model_name='flan-t5',
       model_id='path_to_your_local_model',  # Replace with your local model path
       embedded=False,
       callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])
   )
   
   # Invoke the model with a prompt
   response = llm._call("The first man on the moon was ...")
   print(response)

Replace "path_to_your_local_model" with the actual path to your local ModelScope model. This will load the model and allow you to use it for text generation.

For more detailed instructions, you can refer to the LangChain documentation ^[1][2].

To continue talking to Dosu, mention @dosu.

[LiuChao888]

ok, thank you for your reply again

[LiuChao888]

that is a error

Traceback (most recent call last):
  File "/home/jh/liuchao_test/langc_modelscope.py", line 22, in <module>
    llm = OpenLLM(
          ^^^^^^^^
  File "/home/jh/anaconda3/envs/liuchao/lib/python3.11/site-packages/langchain_community/llms/openllm.py", line 173, in __init__
    runner = openllm.Runner(
             ^^^^^^^^^^^^^^
AttributeError: module 'openllm' has no attribute 'Runner'

python 3.11
langchain 0.2.8
langchain_community 0.2.7

so ,how can I slove zhe error.
thanks

[dosubot[bot]]

To resolve the "AttributeError: module 'openllm' has no attribute 'Runner'" error, follow these steps:

1. Ensure openllm is correctly installed:

   pip install openllm

2. Verify the openllm module:

   import openllm
   print(dir(openllm))

3. Update your code:

   from langchain_community.llms import OpenLLM
   
   llm = OpenLLM(
       model_name='flan-t5',
       model_id='path_to_your_local_model',  # Replace with your local model path
   )
   response = llm.invoke("The first man on the moon was ...")
   print(response)

4. Check for updates: Ensure you are using the latest version of openllm.

If the Runner class is missing, it might be due to changes in the openllm library. The Runner should be initialized with the init_local parameter set to embedded, and the ensure_available parameter set to True:

runner = openllm.Runner(
    model_name='flan-t5',
    model_id='path_to_your_local_model',
    init_local='embedded',
    ensure_available=True,
)

If the issue persists, you may need to refer to the official documentation or repository for the correct usage and any updates regarding the Runner class ^[1].

To continue talking to Dosu, mention @dosu.

[LiuChao888]

now ,I load modes through ollama， because I cant not use many fuctions.