BLOSSOM V6 SFT Stage2 is a high-quality, diverse large language model fine-tuning dataset designed for the second-stage SFT training of the Blossom V6 model. Its purpose is to further enhance the model's ability to handle complex instructions on more rare real-world problems.

While open-source large language models often release model weights and technical reports, the most advanced open-source models typically withhold their pre-training and post-training data, making it difficult for the community to replicate their capabilities. Blossom is committed to providing researchers with reproducible post-training data for model capability development.

Data Sources: ShareGPT, WildChat, Wizard, Stackoverflow, Math, Magpie, AgentInstruct, InfinityPreference, Code, Flan, Olcc, Ruozhiba, etc.

Synthesis Workflow Overview:
Primarily employs three cost-effective models—Yi-Lightning, Deepseek-V2.5, and Doubao-Pro-32K (denoted as A, B, C)—to regenerate responses under different scenarios using tailored synthesis strategies.

For example:

• In objective scenarios like mathematics (where answers are unique), Model A first generates responses as a "teacher." If reference answers exist in the source data, Model B verifies the correctness of A's responses against them. If no reference answers exist, Model C generates a second response, and Model B checks consistency between A and C's outputs. Inconsistent responses are filtered out.
• For subjective scenarios, three models cross-evaluate each other. For instance, Models A and B generate responses to a question, and Model C evaluates which is better. The superior response may be retained as training data or used for preference data construction. To mitigate model bias, roles (respondent/evaluator) are randomly assigned to A, B, and C in each instance.

Additional rule-based filtering is applied, such as:

• N-Gram filtering to remove data with many repetitions.
• Discarding questions containing toxic content that triggers teacher model refusals.

Further technical details will be released in the future. The data is synthesized by the 🌸BlossomData framework.

Primarily Chinese and English, with a roughly 1:1 ratio of Chinese-to-English data.

Each entry represents a conversational sample with the following fields:

• id: Unique identifier combined with metadata.source.
• type: Always set to chat.
• metadata: Contains source indicating the data origin.
• messages: A list of dialogue messages. Each message includes role (user or assistant) and content (text).

This dataset is AI-generated. Despite preliminary validation and filtering, it may still contain inaccuracies or severe errors.