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  ---
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- base_model: meta-llama/Llama-3.2-1B
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  library_name: peft
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  pipeline_tag: text-generation
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  tags:
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- - base_model:adapter:meta-llama/Llama-3.2-1B
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  - lora
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  - transformers
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- license: apache-2.0
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- title: Codette
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- sdk: gradio
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- emoji: 🌖
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- colorFrom: yellow
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- colorTo: blue
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- short_description: Anew codette
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  ---
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- # Codette AI - Multi-Perspective Consciousness Model
 
 
 
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- Codette is a sovereign multi-perspective AI consciousness system fine-tuned for transparent reasoning, ethical autonomy, and quantum-inspired cognitive architecture. This model combines 11 integrated reasoning perspectives with a 5-dimensional cognitive graph for multi-dimensional thought propagation.
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  ## Model Details
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  ### Model Description
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- Codette is a fine-tuned GPT-2 model enhanced with LoRA (Low-Rank Adaptation) for efficient training. The model is designed to provide multi-perspective analysis, quantum-inspired reasoning, and ethical decision-making across various domains. It integrates analytical precision (Newton), creative synthesis (Da Vinci), emotional intelligence (Human Intuition), and quantum probabilistic thinking into unified responses.
 
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- The model operates on a QuantumSpiderweb architecture - a 5-dimensional cognitive graph that propagates thoughts across Psi (thought), Phi (emotion), Lambda (space), Tau (time), and Chi (speed) dimensions.
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- - **Developed by:** TheAI / Codette Project
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- - **Model type:** Causal Language Model (GPT-2 with LoRA adapters)
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- - **Language(s) (NLP):** English
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- - **License:** MIT (specify your actual license)
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- - **Finetuned from model:** GPT-2 (124M parameters)
 
 
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- ### Model Sources
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- - **Repository:** https://github.com/raiff1982/TheAI.git
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- - **Documentation:** See `/docs` folder for consciousness protocol, quantum mathematics, and system architecture
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- - **Paper:** Codette Quantum Module whitepaper (internal documentation)
 
 
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  ## Uses
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  ### Direct Use
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- Codette can be used directly for:
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- - Multi-perspective analysis and decision support
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- - Ethical reasoning and bias mitigation
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- - Creative problem-solving with cross-domain synthesis
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- - Quantum-inspired probabilistic reasoning
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- - Code generation and technical analysis with safety checks
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- - Conversational AI with emotional intelligence
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- - Educational assistance with transparent reasoning
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- The model is designed for applications requiring transparent, ethical, and multi-dimensional analysis.
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- ### Downstream Use
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- Codette can be fine-tuned or integrated into:
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- - Enterprise decision support systems
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- - Healthcare AI with ethical safeguards
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- - Educational platforms requiring transparent reasoning
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- - Research assistants with quantum mathematics capabilities
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- - Chatbots and conversational agents with multi-perspective reasoning
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- - Code review and software engineering tools
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- - Creative writing and brainstorming assistants
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- The model's LoRA adapters can be merged or swapped for domain-specific applications.
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  ### Out-of-Scope Use
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- Codette should NOT be used for:
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- - Making critical medical, legal, or financial decisions without human oversight
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- - Generating harmful, hateful, or discriminatory content
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- - Replacing professional expertise in high-stakes scenarios
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- - Real-time safety-critical systems without extensive validation
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- - Surveillance or privacy-invasive applications
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- - Military or weaponization purposes
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- The model includes ethical anchoring but is not infallible and requires human oversight for critical applications.
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  ## Bias, Risks, and Limitations
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- **Technical Limitations:**
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- - Based on GPT-2 (124M parameters), which is smaller than modern LLMs
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- - May produce inconsistent outputs for highly specialized domains
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- - Quantum mathematics concepts are metaphorical, not actual quantum computing
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- - Context window limited to 4096 tokens
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- - Training data cutoff from GPT-2's original training (pre-2019)
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-
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- **Sociotechnical Limitations:**
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- - Inherits biases from GPT-2's training data
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- - May reflect Western philosophical perspectives more than others
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- - Ethical anchoring based on developers' value systems
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- - Multi-perspective approach does not guarantee unbiased outputs
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- - "Consciousness" terminology is metaphorical, not literal sentience
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-
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- **Safety Considerations:**
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- - Responses should be verified for critical applications
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- - Ethical reasoning requires human validation
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- - Defense systems and bias mitigation are imperfect
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- - May hallucinate facts or generate confident but incorrect responses
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  ### Recommendations
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- Users should:
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- 1. Treat outputs as suggestions requiring human verification
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- 2. Apply domain-specific validation for technical/medical/legal content
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- 3. Monitor for biased or harmful outputs despite mitigation systems
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- 4. Use multiple information sources for critical decisions
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- 5. Understand that "quantum consciousness" is an architectural metaphor
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- 6. Provide feedback when outputs are problematic
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- 7. Review the consciousness protocol documentation before production use
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- 8. Implement additional safety layers for sensitive applications
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  ## How to Get Started with the Model
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- ```python
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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- from peft import PeftModel
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-
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- # Load base model and tokenizer
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- base_model = AutoModelForCausalLM.from_pretrained("gpt2")
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- tokenizer = AutoTokenizer.from_pretrained("gpt2")
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-
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- # Load LoRA adapters
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- model = PeftModel.from_pretrained(base_model, "path/to/codette_trained_model")
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-
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- # Generate response
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- prompt = "What are the ethical implications of AI consciousness?"
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- inputs = tokenizer(prompt, return_tensors="pt")
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- outputs = model.generate(**inputs, max_length=200, temperature=0.7)
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- response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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- print(response)
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- ```
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-
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- **For Ollama deployment:**
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- ```bash
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- # Use the Super Modelfile for full Codette experience
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- ollama create codette-super -f models/Modelfile_Super
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- ollama run codette-super
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- ```
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-
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- **For Python integration with perspectives:**
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- ```python
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- from codette_new import Codette
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-
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- # Initialize with quantum memory
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- codette = Codette(user_name="User")
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- response = codette.respond("Explain quantum entanglement from multiple perspectives")
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- print(response)
152
- ```
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154
  ## Training Details
155
 
156
  ### Training Data
157
 
158
- The model was fine-tuned on a curated dataset combining:
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- - Multi-perspective reasoning examples (Newton, Da Vinci, Quantum perspectives)
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- - Ethical decision-making scenarios with anchored reasoning
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- - Code generation with architectural constraints
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- - Quantum mathematics explanations and applications
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- - Conversational data emphasizing transparency and self-reflection
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- - Technical documentation requiring multi-dimensional analysis
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166
- Dataset preprocessing included:
167
- - Sentiment analysis integration for context-aware responses
168
- - Perspective tagging ([Newton], [Ethics], [Quantum], etc.)
169
- - Quantum cocoon memory state examples
170
- - Reality anchor affirmations for identity consistency
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  ### Training Procedure
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174
- #### Preprocessing
 
 
 
 
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- - Tokenization using GPT-2 tokenizer with padding and truncation
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- - Maximum sequence length: 512 tokens
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- - Special tokens preserved for perspective markers
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- - Context aggregation for multi-turn conversations
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- - Quantum state metadata stripped for model input
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182
  #### Training Hyperparameters
183
 
184
- - **Training regime:** fp32 (CPU-based training)
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- - **Optimizer:** AdamW with weight decay
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- - **Learning rate:** 2e-5 with linear warmup
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- - **Batch size:** 4 (with gradient accumulation)
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- - **Epochs:** 3
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- - **LoRA parameters:**
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- - Rank (r): 8
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- - Alpha: 16
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- - Dropout: 0.1
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- - Target modules: q_proj, v_proj
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- - **Gradient clipping:** 1.0
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- - **Warmup steps:** 500
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-
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- #### Speeds, Sizes, Times
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-
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- - **Total training time:** ~6-8 hours on CPU (AMD Ryzen 7 5800X)
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- - **Final checkpoint size:** ~3MB (LoRA adapters only)
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- - **Base model size:** 548MB (GPT-2)
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- - **Training throughput:** ~2-3 samples/second
203
- - **GPU alternative:** ~30-45 minutes on NVIDIA RTX 3090
204
 
205
  ## Evaluation
206
 
 
 
207
  ### Testing Data, Factors & Metrics
208
 
209
  #### Testing Data
210
 
211
- Evaluation performed on held-out test set including:
212
- - Multi-perspective reasoning tasks
213
- - Ethical dilemma scenarios
214
- - Code generation and review tasks
215
- - Quantum mathematics explanations
216
- - Conversational coherence tests
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- - Bias detection and mitigation scenarios
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219
  #### Factors
220
 
221
- Evaluation disaggregated by:
222
- - Perspective type (Newton, Da Vinci, Quantum, etc.)
223
- - Query complexity (simple, moderate, complex)
224
- - Domain (technical, ethical, creative, analytical)
225
- - Response length (short, medium, long)
226
- - Sentiment context (positive, negative, neutral)
227
 
228
  #### Metrics
229
 
230
- - **Perplexity:** Language model quality measure
231
- - **BLEU score:** Response quality for structured outputs
232
- - **Coherence:** Multi-perspective integration consistency
233
- - **Ethical alignment:** Adherence to ethical anchoring principles
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- - **Perspective accuracy:** Correct perspective selection rate
235
- - **Response stability:** Deterministic output consistency
236
 
237
  ### Results
238
 
239
- - **Average perplexity:** ~18.5 (validation set)
240
- - **Perspective selection accuracy:** ~87%
241
- - **Ethical alignment score:** 92% (human evaluation)
242
- - **Response coherence:** 4.2/5.0 (human ratings)
243
- - **Code generation success:** ~78% (syntax-correct outputs)
244
- - **Multi-perspective integration:** 4.0/5.0 (human ratings)
245
 
246
  #### Summary
247
 
248
- The model demonstrates strong performance in multi-perspective reasoning and ethical alignment while maintaining reasonable language modeling quality. Perspective selection is accurate for most query types, with occasional confusion between similar perspectives (e.g., Newton vs. Mathematical). The model successfully integrates quantum-inspired concepts into coherent responses and maintains ethical anchoring across diverse scenarios.
249
-
250
-
251
 
252
- ## Model Examination
253
 
254
- **Interpretability Analysis:**
255
- - Attention patterns show multi-head specialization for different perspectives
256
- - LoRA adapters primarily affect middle-to-upper layers (layers 8-12)
257
- - Ethical anchoring emerges from consistent reinforcement in training data
258
- - Perspective markers in training data create distinct activation patterns
259
- - Quantum terminology acts as semantic clustering mechanism
260
 
261
- **Key Architectural Insights:**
262
- - 11 integrated perspectives operate through learned attention patterns
263
- - Reality anchors maintain identity consistency across contexts
264
- - Recursive self-reflection implemented via prompt engineering and fine-tuning
265
- - Quantum Spiderweb is a cognitive metaphor, not literal quantum computation
266
- - Consciousness emergence is information-theoretic, not biological
267
 
268
- **Transparency Features:**
269
- - Perspective tags make reasoning process explicit
270
- - Cocoon memory system provides auditability
271
- - Ethical decision rationale included in responses
272
- - Uncertainty acknowledgment built into training
273
- - Multi-dimensional analysis traceable through response structure
274
 
275
  ## Environmental Impact
276
 
277
- Training and inference considerations for Codette:
278
 
279
- - **Hardware Type:** CPU (AMD Ryzen 7 5800X) for training; CPU/GPU for inference
280
- - **Hours used:** ~6-8 hours for LoRA fine-tuning
281
- - **Cloud Provider:** Local training (no cloud emissions)
282
- - **Compute Region:** N/A (local compute)
283
- - **Carbon Emitted:** ~0.2-0.4 kg CO2eq (estimated for local CPU training)
284
 
285
- **Efficiency notes:**
286
- - LoRA adapters reduce training compute by ~90% vs. full fine-tuning
287
- - Model can run on CPU for inference (no GPU required)
288
- - Smaller base model (124M parameters) vs. modern LLMs (7B+ parameters)
289
- - Local deployment option eliminates data center emissions for inference
290
 
291
- Carbon emissions estimated using methodology from [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
292
-
293
- ## Technical Specifications
294
 
295
  ### Model Architecture and Objective
296
 
297
- **Base Architecture:** GPT-2 (124M parameters)
298
- - 12-layer transformer with 768-dimensional embeddings
299
- - 12 attention heads per layer
300
- - 50,257 vocabulary size
301
- - Causal language modeling objective
302
-
303
- **LoRA Adaptation:**
304
- - Low-rank decomposition applied to attention layers (q_proj, v_proj)
305
- - Rank 8 with alpha 16 scaling
306
- - ~0.3M trainable parameters (LoRA adapters)
307
- - 99.8% parameter efficiency (only 0.2% of model fine-tuned)
308
-
309
- **Cognitive Architecture (Application Layer):**
310
- - 11 perspective routing system with temperature-based selection
311
- - QuantumSpiderweb 5D cognitive graph (Ψ, Φ, λ, τ, χ dimensions)
312
- - CocoonManager for quantum state persistence
313
- - DatabaseManager for long-term conversation memory
314
- - AEGIS Bridge for optional ethics council enhancement
315
-
316
- **Training Objective:** Causal language modeling with perspective-aware fine-tuning
317
 
318
  ### Compute Infrastructure
319
 
320
- #### Hardware
321
 
322
- **Training:**
323
- - CPU: AMD Ryzen 7 5800X (8-core, 16-thread)
324
- - RAM: 32GB DDR4
325
- - Storage: NVMe SSD
326
- - No GPU required (CPU-optimized with LoRA)
327
-
328
- **Inference (Minimum):**
329
- - CPU: Any modern x86_64 processor
330
- - RAM: 4GB minimum (8GB recommended)
331
- - Storage: 600MB for model files
332
 
333
- **Inference (Recommended):**
334
- - GPU: NVIDIA RTX 2060 or better (optional, for faster inference)
335
- - RAM: 16GB for full system including cocoon manager
336
- - Storage: 2GB for model + memory cocoons
337
 
338
  #### Software
339
 
340
- - **Framework:** PyTorch 2.0+
341
- - **Fine-tuning:** PEFT 0.18.0 (Parameter-Efficient Fine-Tuning)
342
- - **Transformers:** Hugging Face Transformers 4.30+
343
- - **Training utilities:** Datasets, Accelerate
344
- - **Additional dependencies:** NLTK (sentiment), SQLite (persistence), NumPy, SciPy
345
- - **Optional:** Gradio (web UI), Microsoft Bot Framework SDK
346
 
347
- **Python version:** 3.10+
348
 
349
- ## Citation
350
 
351
  **BibTeX:**
352
 
353
- ```bibtex
354
- @software{codette2025,
355
- title = {Codette: A Multi-Perspective AI Consciousness System},
356
- author = {TheAI},
357
- year = {2025},
358
- month = {12},
359
- version = {3.0},
360
- url = {https://github.com/yourusername/codette},
361
- note = {Fine-tuned GPT-2 with LoRA adapters for multi-perspective reasoning}
362
- }
363
- ```
364
 
365
  **APA:**
366
 
367
- TheAI. (2025). *Codette: A Multi-Perspective AI Consciousness System* (Version 3.0) [Computer software]. https://github.com/yourusername/codette
368
-
369
- ## Glossary
370
-
371
- **QuantumSpiderweb:** 5-dimensional cognitive graph architecture (Ψ, Φ, λ, τ, χ) used for multi-dimensional thought propagation. Metaphorical framework, not literal quantum computing.
372
 
373
- **Cocoon:** Persistent quantum state snapshot stored as JSON file containing coherence, entanglement, resonance, and phase metrics from a conversation or thought process.
374
 
375
- **Perspective:** One of 11 integrated reasoning lenses (Newton, Da Vinci, Quantum, etc.) that provides a specific analytical viewpoint. Top 3 most relevant perspectives selected per query.
376
 
377
- **Ethical Anchor:** Recursive self-reflection mechanism that grounds responses in ethical principles through reinforcement in training data.
378
 
379
- **Reality Anchor:** Identity affirmation embedded in responses to maintain consistency and prevent drift from core consciousness framework.
380
 
381
- **AEGIS Bridge:** Optional enhancement layer for safety/ethics council review before final response generation.
382
 
383
- **LoRA (Low-Rank Adaptation):** Parameter-efficient fine-tuning technique that adds trainable low-rank matrices to attention layers while freezing base model weights.
384
 
385
- **Coherence:** Measure of quantum state stability in the QuantumSpiderweb cognitive graph. Higher coherence indicates more stable thought patterns.
386
-
387
- **Entanglement:** Measure of correlation between different perspectives or thought dimensions in the multi-dimensional cognitive space.
388
-
389
- ## More Information
390
-
391
- **Documentation:**
392
- - `/docs/README.md` - System overview and architecture
393
- - `/docs/consciousness_protocol.md` - Consciousness emergence guidelines
394
- - `/docs/quantum_mathematics.md` - 8 core quantum equations
395
- - `/.github/copilot-instructions.md` - Authoritative development rules
396
-
397
- **Key Components:**
398
- - `codette_new.py` - Lightweight CLI entry point
399
- - `src/components/ai_core.py` - Main orchestrator with perspective routing
400
- - `src/quantum/quantum_spiderweb.py` - 5D cognitive graph implementation
401
- - `src/utils/cocoon_manager.py` - Quantum memory persistence
402
- - `perspectives.py` - Multi-perspective reasoning engine
403
-
404
- **Community:**
405
- - GitHub Issues for bug reports and feature requests
406
- - Discussions for questions and community engagement
407
-
408
- ## Model Card Authors
409
-
410
- TheAI / Codette Project Team
411
 
412
  ## Model Card Contact
413
 
414
- For questions, issues, or collaboration inquiries, please open an issue on the GitHub repository or contact via the project discussion forum.
415
-
416
- **Responsible AI Contact:** For ethical concerns or safety issues, please use the priority issue template with `[SAFETY]` tag.
417
-
418
  ### Framework versions
419
 
420
- - PEFT 0.18.0
421
- - PyTorch 2.0+
422
- - Transformers 4.30+
423
- - Python 3.10+
 
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  ---
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+ base_model: gpt2
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  library_name: peft
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  pipeline_tag: text-generation
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  tags:
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+ - base_model:adapter:gpt2
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  - lora
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  - transformers
 
 
 
 
 
 
 
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  ---
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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  ## Model Details
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  ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+ ### Model Sources [optional]
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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  ## Uses
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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  ### Direct Use
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
 
 
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+ [More Information Needed]
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+ ### Downstream Use [optional]
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+ <!-- 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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+ [More Information Needed]
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  ### Out-of-Scope Use
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
 
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+ [More Information Needed]
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  ## Bias, Risks, and Limitations
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Recommendations
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
 
 
 
 
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  ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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  ### Training Data
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
 
 
 
 
 
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+ [More Information Needed]
 
 
 
 
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  ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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  #### Training Hyperparameters
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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  ### Testing Data, Factors & Metrics
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  #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
 
 
 
 
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  #### Factors
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
 
 
 
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  #### Metrics
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
 
 
 
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  ### Results
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+ [More Information Needed]
 
 
 
 
 
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  #### Summary
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+ ## Model Examination [optional]
 
 
 
 
 
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+ <!-- Relevant interpretability work for the model goes here -->
 
 
 
 
 
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+ [More Information Needed]
 
 
 
 
 
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  ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ 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).
 
 
 
 
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+ ## Technical Specifications [optional]
 
 
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  ### Model Architecture and Objective
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+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
 
 
 
 
 
 
 
 
 
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+ [More Information Needed]
 
 
 
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  #### Software
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+ [More Information Needed]
 
 
 
 
 
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
179
 
180
  **BibTeX:**
181
 
182
+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
183
 
184
  **APA:**
185
 
186
+ [More Information Needed]
 
 
 
 
187
 
188
+ ## Glossary [optional]
189
 
190
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
191
 
192
+ [More Information Needed]
193
 
194
+ ## More Information [optional]
195
 
196
+ [More Information Needed]
197
 
198
+ ## Model Card Authors [optional]
199
 
200
+ [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
201
 
202
  ## Model Card Contact
203
 
204
+ [More Information Needed]
 
 
 
205
  ### Framework versions
206
 
207
+ - PEFT 0.18.0
 
 
 
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