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llmware
/
dragon-llama-answer-tool

Transformers
GGUF
llama
conversational
Model card Files
xet
 Community
How to use from
llama.cpp
Install from brew 
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmware/dragon-llama-answer-tool
# Run inference directly in the terminal:
llama-cli -hf llmware/dragon-llama-answer-tool
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf llmware/dragon-llama-answer-tool
# Run inference directly in the terminal:
llama-cli -hf llmware/dragon-llama-answer-tool
Use pre-built binary 
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf llmware/dragon-llama-answer-tool
# Run inference directly in the terminal:
./llama-cli -hf llmware/dragon-llama-answer-tool
Build from source code 
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf llmware/dragon-llama-answer-tool
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmware/dragon-llama-answer-tool
Use Docker 
docker model run hf.co/llmware/dragon-llama-answer-tool
Quick Links

Model Card for Model ID

dragon-llama-answer-tool is a quantized version of DRAGON Llama 7B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.

dragon-llama-7b 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-llama-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-llama-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: Llama 2 Community License
  • Quantized from model: llmware/dragon-llama

Model Card Contact

Darren Oberst & llmware team

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GGUF
Model size
7B params
Architecture
llama
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