Development of AliceAI-80B-A3B Instruct Model
The development of the yandex/AliceAI-80B-A3B instruct model has reached a new milestone following a reported post-training update. According to a developer update shared on the Reddit community r/LocalLLaMA, the project aims to create an instruct fine-tune of the AliceAI-80B-A3B-Base model, specifically designed for conversational capabilities and agentic workflows.
The project utilizes a distillation method, where the developer is attempting to create a “shallow distill” of the Qwen 3.8 27B model to impart chain-of-thought reasoning and conversational skills to the larger 80B parameter model. The training process is being conducted locally using three 32GB V100 GPUs.
Technical Challenges and Underfitting Issues
In a recent progress report, the developer noted that while the first round of Supervised Fine-Tuning (SFT) was completed, the resulting checkpoint—referred to as “1.0 alpha”—was deemed insufficient for public release.
The developer reported that the initial 5 million tokens used in the training phase lacked the necessary breadth to facilitate general conversation. While the model demonstrated an ability to adopt chain-of-thought reasoning, it struggled with ambiguous prompts or queries that deviated from the exact matches in the training data, resulting in “garbled output.” This indicates the model was significantly underfit for general instruction-following tasks.
To address these limitations, the developer has pivoted from a coding-oriented dataset to a broader general instruct dataset. This new dataset will consist of approximately 5 million tokens. The developer intends to train this new data on top of the existing 1.0 alpha checkpoint using a reduced learning rate.
Data Generation and Community Resources
To accelerate the production of training data, the developer has increased the speed of a local synthetic data generator from 80 tokens per second (tps) to approximately 240 tps by implementing a feature that draws from multiple base URLs.
The developer also highlighted an open-source tool created for this project: sftmill, an off-policy distillation engine hosted on GitHub. According to the developer, this tool is designed to generate raw training data from specific behavioral goals, such as transforming a general instruct requirement into usable data.
Community Engagement and Future Steps
The technical challenges faced during the training process have prompted discussions within the developer community regarding resource allocation. Some users on Reddit have suggested utilizing existing large-scale datasets, such as the Step-3.5-Flash SFT dataset, as an alternative to distilling from Qwen.
Additionally, community members have inquired about the possibility of contributing distributed computing power to assist with dataset creation. The developer indicated that while they will evaluate the necessity of community-contributed compute in the next stage, the current bottleneck remains the speed of dataset generation rather than the model training itself. No official release date has been set for the first functional edition of the model.