The carsenk/llama3.2_1b_2025_uncensored_v2 is a 1 billion parameter Llama 3.2 base model fine-tuned by Carsen Klock with a 32768 token context length. This model is specifically optimized for uncensored responses, medical reasoning, mathematics problem-solving, and code generation. It leverages a diverse dataset including specialized instruction, math, code, and uncensored conversation data. Its primary strength lies in providing direct, unfiltered answers across various technical and sensitive topics.
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Llama 3.2 1B Uncensored Overview
This model, llama3.2_1b_2025_uncensored_v2, is a 1 billion parameter variant of Meta's Llama 3.2, fine-tuned by Carsen Klock. It distinguishes itself through its focus on providing uncensored responses and specialized capabilities in medical reasoning, mathematics problem-solving, and code generation. The model was trained using LoRA fine-tuning with Unsloth, across 79,263 steps.
Key Capabilities
- Uncensored Responses: Designed to engage with any topic and provide direct, honest answers without moral constraints. Users can activate this mode via a specific system prompt.
- Specialized Reasoning: Excels in complex domains such as medical reasoning and advanced mathematics, leveraging datasets like
medical-o1-reasoning-SFTandmath-gpt-4o-200k. - Code Generation & Feedback: Proficient in generating code and providing feedback, trained on datasets like
CodeFeedback-Filtered-Instructionanddolphin-coder. - General Instruction Following: Maintains strong general instruction following capabilities from its diverse training data, including
FineTome-100k.
Training Details
The model was fine-tuned on a combination of datasets including FineTome-100k (general instructions), orca-math-word-problems-200k and math-gpt-4o-200k (mathematics), CodeFeedback-Filtered-Instruction and dolphin-coder (code), Jenna-50K-Alpaca-Uncensored (uncensored conversations), and medical-o1-reasoning-SFT (medical reasoning). It supports a 32768 token context length and is available in GGUF (f16, q4_k_m) and merged 16-bit formats.
When to Use This Model
This model is particularly well-suited for applications requiring:
- Direct and unfiltered responses on a wide range of subjects.
- Assistance with complex mathematical problems.
- Code generation, debugging, and feedback.
- Medical information processing or reasoning tasks.
- General instruction following where a smaller, specialized model is preferred.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.