tiny Qwen2ForCausalLM R1 Distill

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

Released August 2026131K context≈ 98,304 words

A compact, text-only model distilled from reasoning-focused training, this tiny variant trades raw capability for a very small footprint. It inherits some chain-of-thought tendencies from its R1 distillation lineage, though its limited parameter count means depth and reliability will be constrained compared to larger relatives. Likely intended for testing or experimental pipelines rather than production use.

Capabilities

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

Instruction Following

Moderate

Tool Use

tiny Qwen2ForCausalLM R1 Distill

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

Released August 2026131K context≈ 98,304 words

A compact, text-only model distilled from reasoning-focused training, this tiny variant trades raw capability for a very small footprint. It inherits some chain-of-thought tendencies from its R1 distillation lineage, though its limited parameter count means depth and reliability will be constrained compared to larger relatives. Likely intended for testing or experimental pipelines rather than production use.

Capabilities

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

Instruction Following

Moderate

Tool Use

Glossary

Chain-of-ThoughtA reasoning technique where an AI model shows its step-by-step thinking process before arriving at a final answer, making its logic transparent and verifiable.DistillationA technique that compresses a large, complex model into a smaller one by training it to mimic the larger model's behavior, resulting in faster inference with minimal loss of quality.DistilledA model that has been compressed by training a smaller model to mimic a larger, more capable model, reducing size and computational requirements while retaining performance.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning-FocusedA model specifically trained to work through multi-step logical problems methodically rather than generating quick responses.Text-Only ModelA model that processes and produces only text input and output, without support for images, audio, or other data types.Text-Only ModelA language model that processes and generates only text, without support for images, audio, or other data types.

Capabilities

Capabilities

Use Case Fit

Glossary