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**SUS-Chat-34B** is a 34B bilingual Chinese-English dialogue model,
jointly released by the **[Southern University of Science and
Technology](https://huggingface.co/SUSTech)** and
**[IDEA-CCNL](https://huggingface.co/IDEA-CCNL)**. This model is based
on [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B) and has been
fine-tuned on millions of high-quality, multilingual instruction data.
While maintaining the strong language capabilities of the base model,
the SUS-Chat-34B model has improved the modelās response to human
instructions through high-quality instruction fine-tuning and excels at
imitating human thought processes through chains of thought. It
introduces inter-instruction attention sharing in long texts, expanding
the window size from 4K to 8K, significantly enhancing the usability of
multi-turn dialogues.
It has surpassed all models of the same size in almost all benchmark
tests and is better suited to meet the practical needs of complex
multilingual tasks. Compared to larger models, SUS-Chat-34B remains
highly competitive and has achieved state-of-the-art performance in our
comprehensive evaluations.
SUS-Chat-34B model has the following highlights:
1. Large-scale complex instruction following data: Trained with 1.4
billion tokens of high-quality complex instruction data, covering
Chinese and English, multi-turn dialogues, mathematics, reasoning,
and various other types of instruction data;
2. Strong performance in general tasks: The SUS-Chat-34B model excels
in numerous mainstream Chinese and English tasks, surpassing other
open-source instruction fine-tuned models of the same parameter
scale. It also competes well against models with larger parameter
scales;
3. Longer context window and excellent multi-turn dialogue
capabilities: Currently, SUS-Chat-34B supports an 8K context window,
and is trained with a large amount of multi-turn instruction and
single-multi-turn mixed data, demonstrating remarkable capabilities
in long-text dialogue information focus and instruction follow-up.
SUS-Chat powerfully demonstrates that through the right instruction
fine-tuning, academic institutions can achieve better performance
without increasing model parameters, using open-source datasets and
models. This bridges the gap between academia and industry in large
language models and opens new possibilities for collaboration between
academic and industrial sectors.
# Performance
To better evaluate the performance of the SUS-Chat-34B model, we
conducted assessments across multiple benchmark tests and have
open-sourced the evaluation framework
[TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate
replication and comparison by other researchers.
In TLEM, we utilized various benchmark tests including MMLU, CMMLU,
C-Eval, BBH, GSM-8K, and MATH, to measure the modelās knowledge and
thinking capabilities. In these metrics, the SUS-Chat-34B model achieved
state-of-the-art performance. Additionally, we incorporated
[lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test
SUS-Chat and similar models on winogrande, hellaswag, arc, and
truthful-qa, assessing the modelās common-sense reasoning ability and
susceptibility to illusions.
Overall, the SUS-Chat-34B model significantly outperformed models of
similar scale and achieved the most advanced comprehensive performance.
English Understanding
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Chinese Capabilities
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C-Eval results are evaluated on the validation datasetsā©ļø