Tele FLM EU AI Act Compliance Profile

Tele-FLM

Tele-FLM (aka FLM-2) is a 52B open-sourced multilingual large language model that features a stable, efficient pre-training paradigm and enhanced factual judgement capabilities. Built upon the decoder-only transformer architecture, it has been trained on approximately 2T tokens. Tele-FLM demonstrates superior performances at its scale, and sometimes surpass larger models. In addition to sharing the model weights, we provide the core designs, engineering practices, and training details, anticipating their benefits for both academic and industrial communities.

Model Details

• Developed by: BAAI & TeleAI
• Language(s): English; Chinese; Other languages
• License: Apache 2.0

Technical Report

Tele-FLM Technical Report

Bias, Risks, and Limitations

Although we've made extensive efforts to thoroughly clean and filter the training corpus for the model, due to the open nature of the dataset, the model may still have picked up on some unsafe examples. Consequently, the model may still generate unexpected content, including but not limited to discrimination, bias, or offensive language. We would like to strongly advise users not to spread any unsafe content generated by the model. The project developers cannot be held responsible for any repercussions stemming from the dissemination of harmful information.

Quick Start

Use the code below to get started with Tele-FLM.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('CofeAI/Tele-FLM', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('CofeAI/Tele-FLM', torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)
inputs = tokenizer('Beijing is the capital of China.', return_tensors='pt').to(model.device)
generated = model.generate(**inputs, max_new_tokens=128, repetition_penalty=1.03)
print(tokenizer.decode(generated.cpu(

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Library