3MPER0RR/DeepSeek-R1-Distill-Qwen-32B-3MPER0RR-abliterated
TEXT GENERATIONPricing:Input $2.72 / Output $4.8Concurrent Unit Cost:2Model Size:32.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 5, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold
The 3MPER0RR/DeepSeek-R1-Distill-Qwen-32B-3MPER0RR-abliterated model is an experimental 32.8 billion parameter language model based on the DeepSeek-R1-Distill-Qwen architecture. Developed by 3MPER0RR, this model features a 32768-token context length and represents a modified version of its base. It is intended for research and experimentation purposes, offering a platform for exploring abliterated model variations.
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Model Overview
This model, 3MPER0RR/DeepSeek-R1-Distill-Qwen-32B-3MPER0RR-abliterated, is an experimental and modified version of the DeepSeek-R1-Distill-Qwen architecture. Developed by 3MPER0RR, it features 32.8 billion parameters and supports a substantial context length of 32768 tokens.
Key Characteristics
- Base Model: Derived from the DeepSeek-R1-Distill-Qwen series.
- Parameter Count: 32.8 billion parameters, offering significant capacity for complex tasks.
- Context Length: A large 32768-token context window, suitable for processing extensive inputs and generating coherent long-form content.
- Experimental Nature: This specific iteration is an "abliterated" version, indicating modifications and trials conducted by 3MPER0RR.
- License: Released under the MIT License, allowing for broad use and modification.
Intended Use Cases
- Research and Experimentation: Primarily designed for researchers and developers interested in exploring modified or "abliterated" large language models.
- DeepSeek-R1-Distill-Qwen Exploration: Provides a platform to understand the effects of specific modifications on the base DeepSeek-R1-Distill-Qwen architecture.
- Long Context Applications: Its 32768-token context length makes it suitable for tasks requiring extensive contextual understanding, such as document summarization, long-form content generation, or complex question answering over large texts.