transformer 1.3B 100B

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

Released April 20242K context≈ 1,536 words1.3B params

A compact, openly available transformer model from fla-hub, sitting at 1.3 billion parameters trained on 100 billion tokens. Its small footprint makes it easy to run locally or on modest hardware, though its 2048-token context window and parameter count mean it handles simpler tasks more reliably than complex, multi-step reasoning. Think of it as a lightweight workhorse — straightforward and accessible, but not built for heavy lifting.

Capabilities

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

Instruction Following

Moderate

Multimodal

transformer 1.3B 100B

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

Released April 20242K context≈ 1,536 words1.3B params

A compact, openly available transformer model from fla-hub, sitting at 1.3 billion parameters trained on 100 billion tokens. Its small footprint makes it easy to run locally or on modest hardware, though its 2048-token context window and parameter count mean it handles simpler tasks more reliably than complex, multi-step reasoning. Think of it as a lightweight workhorse — straightforward and accessible, but not built for heavy lifting.

Capabilities

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

Instruction Following

Moderate

Multimodal

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Multi-Step ReasoningThe ability to break down complex problems into smaller steps and solve them sequentially, rather than jumping directly to an answer.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.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.TransformerThe dominant neural network architecture for language models, using self-attention to process sequences.Transformer ModelA neural network architecture that uses attention mechanisms to process sequences of data, allowing it to understand relationships between distant elements in the input.

Capabilities

Capabilities

Use Case Fit

Glossary