Kunoichi DPO v2 7B

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released January 20248K context≈ 6,144 words7B params

Kunoichi DPO v2 7B is a fine-tuned 7B model shaped by Direct Preference Optimization, giving it a conversational style that tends to feel more aligned with human preferences than a raw base model. It sits in the sweet spot of being small enough to run locally while still handling nuanced dialogue and instruction-following reasonably well. The 8K context window is modest, and as a 7B model it will show its limits on complex reasoning tasks.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Coding

Kunoichi DPO v2 7B

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released January 20248K context≈ 6,144 words7B params

Kunoichi DPO v2 7B is a fine-tuned 7B model shaped by Direct Preference Optimization, giving it a conversational style that tends to feel more aligned with human preferences than a raw base model. It sits in the sweet spot of being small enough to run locally while still handling nuanced dialogue and instruction-following reasonably well. The 8K context window is modest, and as a 7B model it will show its limits on complex reasoning tasks.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Coding

Glossary

AlignedA model trained to behave safely and follow human values through techniques like safety filtering and refusal of harmful requests.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.Complex ReasoningThe ability to work through multi-step problems, analyze nuanced information, and draw logical conclusions.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Direct Preference OptimizationA training technique that teaches a model to prefer certain outputs over others by learning from examples of better and worse responses.Direct Preference OptimizationTraining method that aligns models with human preferences by directly optimizing the difference between preferred and dispreferred outputs.Fine-TunedA pre-trained model further trained on a smaller, task-specific dataset to improve performance on that task.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.OptimizationThe process of adjusting model parameters to minimize errors and improve performance.Preference OptimizationA training method that learns from pairwise comparisons between solutions rather than explicit reward signals.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning TasksProblems that require a model to think through multiple steps logically to arrive at an answer, rather than just pattern-matching.

Capabilities

Capabilities

Use Case Fit

Glossary