Orpo Llama 3.2 1B 15k

Llama 3.2

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

Released October 2024131K context≈ 98,304 words1B params

A compact, openly licensed text model fine-tuned using ORPO (Odds Ratio Preference Optimization), a technique that combines supervised fine-tuning and preference alignment in a single training stage. At 1B parameters it operates with a very small memory footprint, making it accessible on modest hardware. The large 131k-token context window is notable for its size class, though generation quality reflects the constraints of a 1B parameter base.

Capabilities

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

Coding

Moderate

Creative Writing

Orpo Llama 3.2 1B 15k

Llama 3.2

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

Released October 2024131K context≈ 98,304 words1B params

A compact, openly licensed text model fine-tuned using ORPO (Odds Ratio Preference Optimization), a technique that combines supervised fine-tuning and preference alignment in a single training stage. At 1B parameters it operates with a very small memory footprint, making it accessible on modest hardware. The large 131k-token context window is notable for its size class, though generation quality reflects the constraints of a 1B parameter base.

Capabilities

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

Coding

Moderate

Creative Writing

Glossary

AlignmentThe process of training a model to behave safely and according to human values and preferences, which base models typically lack.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Fine-TunedA pre-trained model further trained on a smaller, task-specific dataset to improve performance on that task.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.OptimizationThe process of adjusting model parameters to minimize errors and improve performance.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.Preference AlignmentHow well an AI system's judgments match the actual preferences of target users or evaluators.Preference OptimizationA training method that learns from pairwise comparisons between solutions rather than explicit reward signals.Supervised Fine-tuningTraining a model on labeled examples to adapt it for a specific task or domain.Supervised Fine-tuningAdapting a pre-trained model using labeled examples where the correct outputs are known.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary