DRAGON Models
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Production-grade RAG-optimized 6-7B parameter models - "Delivering RAG on ..." the leading foundation base models • 23 items • Updated • 45
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Model Card for Model ID
dragon-yi-answer-tool is a quantized version of DRAGON Yi 6B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
dragon-yi-6b is a fact-based question-answering model, optimized for complex business documents.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/dragon-yi-answer-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
Load in your favorite GGUF inference engine, or try with llmware as follows:
from llmware.models import ModelCatalog
model = ModelCatalog().load_model("dragon-yi-answer-tool")
response = model.inference(query, add_context=text_sample)
Note: please review config.json in the repository for prompt wrapping information, details on the model, and full test set.
Model Description
- Developed by: llmware
- Model type: GGUF
- Language(s) (NLP): English
- License: Yi Community License
- Quantized from model: llmware/dragon-yi
Model Card Contact
Darren Oberst & llmware team
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmware/dragon-yi-answer-tool", filename="dragon-yi.gguf", )