llmware-ai--llmware
wehub-resource-sync
82 行
4.1 KiB
Python
82 行
4.1 KiB
Python
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""" This example illustrates how to use Ollama models in llmware. It assumes that you have separately
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downloaded and installed Ollama and used 'ollama run {model_name}' to cache several models in
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ollama. """
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from llmware.models import ModelCatalog
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# Step 1 - register your Ollama models in llmware ModelCatalog
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# -- these two lines will register: llama2 and mistral models
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# -- note: assumes that you have previously cached and installed both of these models with ollama locally
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# register llama2
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ModelCatalog().register_ollama_model(model_name="llama2",model_type="chat",host="localhost",port=11434)
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# register mistral - note: if you are using ollama defaults, then OK to register with ollama model name only
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ModelCatalog().register_ollama_model(model_name="mistral")
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# optional - confirm that model was registered
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my_new_model_card = ModelCatalog().lookup_model_card("llama2")
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print("\nupdate: confirming - new ollama model card - ", my_new_model_card)
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# Step 2 - start using the Ollama model like any other model in llmware
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print("\nupdate: calling ollama llama 2 model ...")
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model = ModelCatalog().load_model("llama2")
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response = model.inference("why is the sky blue?")
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print("update: example #1 - ollama llama 2 response - ", response)
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# Tip: if you are loading 'llama2' chat model from Ollama, note that it is already included in
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# the llmware model catalog under a different name, "TheBloke/Llama-2-7B-Chat-GGUF"
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# the llmware model name maps to the original HuggingFace repository, and is a nod to "TheBloke" who has
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# led the popularization of GGUF - and is responsible for creating most of the GGUF model versions.
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# --llmware uses the "Q4_K_M" model by default, while Ollama generally prefers "Q4_0"
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print("\nupdate: calling Llama-2-7B-Chat-GGUF in llmware catalog ...")
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model = ModelCatalog().load_model("TheBloke/Llama-2-7B-Chat-GGUF")
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response = model.inference("why is the sky blue?")
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print("update: example #1 - [compare] - llmware / Llama-2-7B-Chat-GGUF response - ", response)
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# Now, let's try the Ollama Mistral model with a context passage
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model2 = ModelCatalog().load_model("mistral")
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context_passage= ("NASA’s rover Perseverance has gathered data confirming the existence of ancient lake "
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"sediments deposited by water that once filled a giant basin on Mars called Jerezo Crater, "
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"according to a study published on Friday. The findings from ground-penetrating radar "
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"observations conducted by the robotic rover substantiate previous orbital imagery and "
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"other data leading scientists to theorize that portions of Mars were once covered in water "
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"and may have harbored microbial life. The research, led by teams from the University of "
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"California at Los Angeles (UCLA) and the University of Oslo, was published in the "
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"journal Science Advances. It was based on subsurface scans taken by the car-sized, six-wheeled "
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"rover over several months of 2022 as it made its way across the Martian surface from the "
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"crater floor onto an adjacent expanse of braided, sedimentary-like features resembling, "
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"from orbit, the river deltas found on Earth.")
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response = model2.inference("What are the top 3 points?", add_context=context_passage)
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print("\nupdate: calling ollama mistral model ...")
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print("update: example #2 - ollama mistral response - ", response)
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# Step 3 - using the ollama discovery API - optional
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discovery = model2.discover_models()
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print("\nupdate: example #3 - checking ollama model manifest list: ", discovery)
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if len(discovery) > 0:
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# note: assumes tht you have at least one model registered in ollama -otherwise, may throw error
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for i, models in enumerate(discovery["models"]):
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print("ollama models: ", i, models)
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# for more information and other alternatives for using GGUF models, please see the following examples:
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# -- examples/Models/chat_gguf_fast_start.py
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# -- examples/Models/using_gguf.py
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# -- examples/Models/using-open-chat-models.py
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# -- examples/Models/dragon-gguf_fast_start.py
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