deepseek math 7b base 4bit

DeepSeek

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

Released October 20194K context≈ 3,072 words

A compact, math-focused model that has been quantized to 4-bit precision, making it lighter on memory at the cost of some numerical fidelity. It operates within a modest 4096-token context window, which suits focused problem-solving but limits longer multi-step derivations. As a base model, it lacks instruction-following fine-tuning, so it responds more like a text completer than a conversational assistant.

Capabilities

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

Reasoning & Logic

Strong

Coding

deepseek math 7b base 4bit

DeepSeek

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

Released October 20194K context≈ 3,072 words

A compact, math-focused model that has been quantized to 4-bit precision, making it lighter on memory at the cost of some numerical fidelity. It operates within a modest 4096-token context window, which suits focused problem-solving but limits longer multi-step derivations. As a base model, it lacks instruction-following fine-tuning, so it responds more like a text completer than a conversational assistant.

Capabilities

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

Reasoning & Logic

Strong

Coding

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.Problem-SolvingThe model's capacity to analyze difficult questions or technical challenges and work toward accurate, well-reasoned solutions.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.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