|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
**bling-answer-tool** is a quantized version of BLING Tiny-Llama 1B, with 4_K_M GGUF quantization, providing a very fast, very small inference implementation for use on CPUs. |
|
|
|
[**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) 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-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("bling-answer-tool") |
|
response = model.inference(query, add_context=text_sample) |
|
|
|
Note: please review [**config.json**](https://huggingface.co/llmware/bling-answer-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. |
|
|
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
- **Developed by:** llmware |
|
- **Model type:** GGUF |
|
- **Language(s) (NLP):** English |
|
- **License:** Apache 2.0 |
|
- **Quantized from model:** [llmware/bling-tiny-llama](https://huggingface.co/llmware/bling-tiny-llama-v0/) |
|
|
|
|
|
## Model Card Contact |
|
|
|
Darren Oberst & llmware team |
