Model Introduction
We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series. The open-source Index-1.9B series includes the following models:
- Index-1.9B base: The base model, with 1.9 billion non-embedding parameters, pre-trained on a 2.8T corpus mainly in Chinese and English. It leads in multiple evaluation benchmarks compared to models of the same level.
- Index-1.9B pure : A control version of the base model with the same parameters and training strategy, but strictly filtered out all instruction-related data from the corpus to verify the impact of instructions on benchmarks.
- Index-1.9B chat (this repository's model) : A dialogue model aligned with SFT and DPO based on the Index-1.9B base. We found that due to the introduction of a lot of internet community corpus in our pre-training, the model has significantly more interesting chatting capabilities.
- Index-1.9B character : Introduces RAG on top of SFT and DPO to achieve few-shots role-playing customization.
Adapted to llamacpp and Ollama, see Index-1.9B-Chat-GGUF
For more details, see our GitHub and Index-1.9B Technical Report
Loading with Transformers
You can load the Index-1.9B-Chat model for dialogue using the following code:
import argparse
from transformers import AutoTokenizer, pipeline
# Attention! The directory must not contain "." and can be replaced with "_".
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', default="IndexTeam/Index-1.9B-Chat", type=str, help="")
parser.add_argument('--device', default="cpu", type=str, help="") # also could be "cuda" or "mps" for Apple silicon
args = parser.parse_args()
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
generator = pipeline("text-generation",
model=args.model_path,
tokenizer=tokenizer, trust_remote_code=True,
device=args.device)
system_message = "你是由哔哩哔哩自主研发的大语言模型，名为“Index”。你能够根据用户传入的信息，帮助用户完成指定的任务，并生成恰当的、符合要求的回复。"
query = "续写 天不生我金坷垃"
model_input = []
model_input.append({"role": "system", "content": system_message})
model_input.append({"role": "user", "content": query})
model_output = generator(model_input, max_new_tokens=300, top_k=5, top_p=0.8, temperature=0.3, repetition_penalty=1.1, do_sample=True)
print('User:', query)
print('Model:', model_output)