--- license: other license_name: license license_link: LICENSE ---

Index-1.9B-Chat

## 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](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF) For more details, see our [GitHub](https://github.com/bilibili/Index-1.9B) and [Index-1.9B Technical Report](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf) ### Loading with Transformers You can load the Index-1.9B-Chat model for dialogue using the following code: ```python 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) ```