TinyLlama 1.1B step 50K 105b

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

Released September 20232K context≈ 1,536 words

A lightweight, no-frills text model that prioritizes accessibility over capability. At 1.1 billion parameters with a 2048-token context window, it operates under tight constraints — expect limited reasoning depth and knowledge compared to larger models. It represents a mid-training checkpoint (step 50K, 105 billion tokens seen), meaning it's not fully trained and behaves accordingly.

Capabilities

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

Factual Knowledge

Basic

Instruction Following

TinyLlama 1.1B step 50K 105b

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

Released September 20232K context≈ 1,536 words

A lightweight, no-frills text model that prioritizes accessibility over capability. At 1.1 billion parameters with a 2048-token context window, it operates under tight constraints — expect limited reasoning depth and knowledge compared to larger models. It represents a mid-training checkpoint (step 50K, 105 billion tokens seen), meaning it's not fully trained and behaves accordingly.

Capabilities

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

Factual Knowledge

Basic

Instruction Following

Glossary

AccessibilityDesigning technology so people with disabilities can use it effectively.CheckpointA saved snapshot of a model's weights and state at a specific point during training, allowing training to resume or the model to be evaluated at that stage.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Mid-Training CheckpointA snapshot of a model saved during training before it has finished learning, allowing you to use or study the model at an intermediate stage rather than waiting for full completion.Mid-trainingA training stage between pretraining and post-training where models are trained on curated, large-scale data mixtures to strengthen specific capabilities.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.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.TokensThe basic units of text that a language model processes, typically representing words or word fragments.Training CheckpointA saved snapshot of the model's learned weights at a specific point during training, allowing you to see how the model improved over time.

TinyLlama 1.1B step 50K 105b

Capabilities

TinyLlama 1.1B step 50K 105b

Capabilities

Use Case Fit

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Glossary