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Small CPU-based RAG-optimized, instruct-following 1B-3B parameter models • 27 items • Updated • 28
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)BLING-QWEN-MINI-TOOL (1.5B)
bling-qwen-mini-tool is a RAG-finetuned version on Qwen2-1.5B for use in fact-based context question-answering, packaged with 4_K_M GGUF quantization, providing a very fast, very small inference implementation for use on CPUs.
Benchmark Tests
Evaluated against the benchmark test: RAG-Instruct-Benchmark-Tester 1 Test Run with sample=False & temperature=0.0 (deterministic output) - 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations.
--Accuracy Score: 93.5 correct out of 100
--Not Found Classification: 75.0%
--Boolean: 87.5%
--Math/Logic: 70.0%
--Complex Questions (1-5): 3 (Average)
--Summarization Quality (1-5): 3 (Average)
--Hallucinations: No hallucinations observed in test runs.
For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo).
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/bling-qwen-mini-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-qwen-mini-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: Apache 2.0
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/bling-qwen-mini-tool", filename="bling-qwen-1-5b.gguf", )