gemma 2 mitra e fp8

Gemma 2

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

Released April 20268K context≈ 6,144 words

A specialized embedding-focused variant of Google's Gemma 2, quantized to FP8 precision by buddhist-nlp. It trades some numerical precision for reduced memory footprint and faster inference, which is typical of FP8 quantization trade-offs. Its output is embeddings rather than generated text, making it suited for representation and retrieval tasks rather than conversational use.

Capabilities

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

Tool Use

Moderate

Multilingual

gemma 2 mitra e fp8

Gemma 2

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

Released April 20268K context≈ 6,144 words

A specialized embedding-focused variant of Google's Gemma 2, quantized to FP8 precision by buddhist-nlp. It trades some numerical precision for reduced memory footprint and faster inference, which is typical of FP8 quantization trade-offs. Its output is embeddings rather than generated text, making it suited for representation and retrieval tasks rather than conversational use.

Capabilities

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

Tool Use

Moderate

Multilingual

Glossary

EmbeddingA dense numerical vector that represents a word, sentence, or concept in a high-dimensional space.EmbeddingsNumerical representations of text that capture semantic meaning, allowing the model to measure similarity between different words or phrases.FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.RetrievalThe process of finding and returning relevant documents or information from a database based on a query.

Capabilities

Capabilities

Use Case Fit

Glossary