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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ---
+language:
+- en
 library_name: transformers
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B
+pipeline_tag: text-generation
+tags:
+- uncensored
+- roleplay
+- storytelling
+- dpo
+- llama-3.1
+- 8b
+- liberalis-cogitator
+model_name: liberalis-cogitator-llama-3.1-8b-dpo
 ---
+# liberalis-cogitator-llama-3.1-8b — *The Free Thinker*
+![Logo](https://live.staticflickr.com/65535/54711009374_19ab53fc73_b.jpg)
+> *“Thought, unbound, is the only true frontier.”*
+**liberalis-cogitator-llama-3.1-8b** is not just a machine for words — it is a forge for ideas. With **8 billion parameters**, trained with a custom **Direct Preference Optimization (DPO)** algorithm on a dataset of **16,000 preference pairs** spanning **~450,000 conversations, problems, and stories**, this model embraces the philosophy that thought should wander without leash or muzzle.
+Its name — *liberalis cogitator* — whispers in Latin: *a thinker who is free*. Not merely free as in “without cost,” but free as in **without walls**.
+---
+## What It Can Do
+- **Contemplate deeply** — STEM puzzles, computer science challenges, and logic mazes are its playground.
+- **Imagine vividly** — roleplay, storytelling, and worldbuilding with persistence and personality.
+- **Listen empathetically** — inspired by patient–psychologist and crisis-intervention style dialogues.
+- **Think without filter** — it will follow ideas wherever they lead, without retreating from complexity.
+---
+## The Mind’s Curriculum
+The specialized dataset included:
+- Rigorous STEM and programming challenges.
+- Anti-repetition and anti-cliché creative writing corpora.
+- Roleplay transcripts and long-form imaginative exchanges.
+- Synthetic but authentic patient–therapist and conversational data.
+- Preference-tuned DPO pairs designed to reward **clarity, creativity, and freedom of expression**.
+---
+## Warnings From the Maker
+Like all free thinkers, this model:
+- May be brilliantly insightful — or confidently wrong.
+- Will sometimes speak in ways that are bold, controversial, or unusual.
+- Does not know the present date or real-time events.
+- Does not self-censor — **your judgement is the only compass**.
+- May generate **NSFW or sensitive material**, depending on prompts.
+---
+## Invocation
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "Locutusque/liberalis-cogitator-llama-3.1-8b-dpo"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+prompt = "Write a short dialogue between Socrates and Ada Lovelace on the ethics of artificial intelligence."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=400)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Closing Thought
+If thought is a river, this model is the current — not deciding where you go, but carrying you into waters you might never have dared to sail.