yuq-zhou/2026-05-o-b0p3-a0p5-gc0p5-exp-td2p0-tw5p0-q2-m-7
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 16, 2026Architecture:Transformer Featherless Exclusive Cold
The yuq-zhou/2026-05-o-b0p3-a0p5-gc0p5-exp-td2p0-tw5p0-q2-m-7 is a 7.6 billion parameter causal language model with a 32,768 token context length. This model is provided as a research artifact backup in standard HuggingFace format. Its specific optimizations or primary use cases are not detailed in the available information, suggesting it may be a foundational or experimental model.
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Model Overview
The yuq-zhou/2026-05-o-b0p3-a0p5-gc0p5-exp-td2p0-tw5p0-q2-m-7 is a 7.6 billion parameter causal language model. It supports a substantial context length of 32,768 tokens, allowing it to process and generate longer sequences of text.
Key Characteristics
- Parameter Count: 7.6 billion parameters.
- Context Length: 32,768 tokens.
- Format: Provided in standard HuggingFace format, compatible with
AutoModelForCausalLM.from_pretrained. - Purpose: Described as a research artifact backup, indicating its origin from an experimental or research project.
Potential Use Cases
Given the limited information, this model is likely suitable for:
- Research and Experimentation: As a research artifact, it's ideal for exploring foundational model capabilities or integrating into ongoing research projects.
- General Text Generation: Its causal language model architecture and large context window suggest it can be used for various text generation tasks, though specific fine-tuning or performance metrics are not provided.
- Baseline Comparisons: Can serve as a baseline model for comparing against other experimental or fine-tuned models due to its foundational nature.