AFM 4.5B Base

Name: AFM 4.5B Base
Author: Arcee AI

by Arcee AI

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

Released October 202466K context≈ 49,152 words4.5B params

A compact, openly licensed base model from Arcee AI that handles text-to-text tasks within a generous 65K token context window. As a base model, it arrives without instruction tuning or chat formatting — it's a raw foundation intended for fine-tuning or further adaptation rather than direct conversational use. Its smaller 4.5B parameter footprint makes it approachable for teams with limited compute.

Capabilities

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

Coding

Moderate

Tool Use

AFM 4.5B Base

by Arcee AI

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

Released October 202466K context≈ 49,152 words4.5B params

A compact, openly licensed base model from Arcee AI that handles text-to-text tasks within a generous 65K token context window. As a base model, it arrives without instruction tuning or chat formatting — it's a raw foundation intended for fine-tuning or further adaptation rather than direct conversational use. Its smaller 4.5B parameter footprint makes it approachable for teams with limited compute.

Capabilities

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

Coding

Moderate

Tool Use

Glossary

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.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Parameter FootprintThe total number of learnable weights in a model, which directly affects its memory requirements and computational cost — smaller footprints run faster on consumer devices.Text-to-TextA framework where all NLP tasks are treated as converting input text into output text, so translation, summarization, and classification use the same model structure.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