llm-rs ggml

GGML converted version of Databricks Dolly-V2 models

Description

Dolly is trained on ~15k instruction/response fine tuning records databricks-dolly-15k generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization.

Converted Models

Name Based on Type Container GGML Version
dolly-v2-12b-f16.bin databricks/dolly-v2-12b F16 GGML V3
dolly-v2-12b-q4_0.bin databricks/dolly-v2-12b Q4_0 GGML V3
dolly-v2-12b-q4_0-ggjt.bin databricks/dolly-v2-12b Q4_0 GGJT V3
dolly-v2-3b-f16.bin databricks/dolly-v2-3b F16 GGML V3
dolly-v2-3b-q4_0.bin databricks/dolly-v2-3b Q4_0 GGML V3
dolly-v2-3b-q4_0-ggjt.bin databricks/dolly-v2-3b Q4_0 GGJT V3
dolly-v2-3b-q5_1.bin databricks/dolly-v2-3b Q5_1 GGML V3
dolly-v2-3b-q5_1-ggjt.bin databricks/dolly-v2-3b Q5_1 GGJT V3
dolly-v2-7b-f16.bin databricks/dolly-v2-7b F16 GGML V3
dolly-v2-7b-q4_0.bin databricks/dolly-v2-7b Q4_0 GGML V3
dolly-v2-7b-q4_0-ggjt.bin databricks/dolly-v2-7b Q4_0 GGJT V3
dolly-v2-7b-q5_1.bin databricks/dolly-v2-7b Q5_1 GGML V3
dolly-v2-7b-q5_1-ggjt.bin databricks/dolly-v2-7b Q5_1 GGJT V3

Usage

Python via llm-rs:

Installation

Via pip: pip install llm-rs

Run inference

from llm_rs import AutoModel

#Load the model, define any model you like from the list above as the `model_file`
model = AutoModel.from_pretrained("rustformers/dolly-v2-ggml",model_file="dolly-v2-12b-q4_0-ggjt.bin")

#Generate
print(model.generate("The meaning of life is"))

Rust via Rustformers/llm:

Installation

git clone --recurse-submodules https://github.com/rustformers/llm.git
cd llm
cargo build --release

Run inference

cargo run --release -- gptneox infer -m path/to/model.bin  -p "Tell me how cool the Rust programming language is:"