BLING Models
Collection
Small CPU-based RAG-optimized, instruct-following 1B-3B parameter models
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27 items
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Updated
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bling-stablelm-3b-gguf is a quantized version of BLING Stable-LM 3B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
bling-stablelm-3b 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/bling-stablelm-3b-gguf", 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("llmware/bling-stablelm-3b-gguf")
response = model.inference(query, add_context=text_sample, add_prompt_engineering="default_with_context")
Note: please review config.json in the repository for prompt wrapping information, details on the model, and full test set.
Darren Oberst & llmware team