metadata
license: cc-by-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- dense-responses
- self-improvement
- representation-engineering
- cf-hot
- recursive-self-improvement
base_model: NousResearch/Hermes-3-Llama-3.1-8B
ARC-Base-8B-Condensed
Adaptive Recursive Cognition
A Multi-Loop Self-Stabilizing Language Model with Predictive Control
Logan Matthew Napolitano
Research into stable self-improving language models
Quick Start • Architecture • Commands • Technical Specification • Citation
Table of Contents
- Model Description
- Quick Start
- Architecture
- Core Technology
- Command Reference
- Evaluation
- Installation
- Configuration
- Repository Structure
- Hardware Requirements
- Training From Scratch
- API Reference
- Limitations
- Ethical Considerations
- Technical Specification
- Changelog
- Citation
- License
Primary Reference
The complete theoretical framework, methodology, and reproducibility details for this model are documented in:
Napolitano, L. M. (2025). Controlled Language Models: Decode-Time Behavioral Control and Token Efficiency.
Zenodo. https://doi.org/10.5281/zenodo.18344021
This paper should be cited for any academic or technical use of ARC-Base-8B-Condensed.
Model Description
ARC-Base-8B-Condensed is a fine-tuned version of Hermes-3-Llama-3.1-8B designed for:
- Dense, information-rich responses — Reduced filler, hedging, and verbosity
- Predictive behavioral control — CF-HoT heads detect and suppress failure modes before they manifest
- Recursive self-improvement — Micro-training with automatic rollback on quality degradation
- Mentor-based learning — Optional consultation with Claude API for continuous improvement
Intended Use
- Research into self-improving language models
- Applications requiring concise, direct responses
- Study of representation engineering and behavioral control
- Base for further fine-tuning experiments
Not Intended For
- Production deployment without evaluation
- Safety-critical applications
- Unsupervised autonomous operation
- Applications requiring verbose, elaborative responses
Quick Start
One-Command Start
git clone https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed
cd ARC-Base-8B-Condensed
pip install -r requirements.txt
python arc_engine_v29_full.py
On first run, the engine will:
- Download the base model (~16GB)
- Load the DENSE adapter and CF-HoT heads
- Initialize all subsystems
- Present an interactive command prompt
═══════════════════════════════════════════════════════════════════════════════
ARC ENGINE v2.9 - Adaptive Recursive Cognition
Multi-Loop Self-Stabilizing Language Model
═══════════════════════════════════════════════════════════════════════════════
DENSE Mode: ON (CONDENSATOR checkpoint)
CF-HoT Control: ON
CF-HoT 125×: OFF
Mentor Mode: OFF
Auto-Train: OFF
Experience Buffer: 0 examples
═══════════════════════════════════════════════════════════════════════════════
You> hello
Hello. How can I help?
[Quality: 0.82 | Density: 45.2 | Coherence: 0.95 | Tokens: 5]
Minimal Python Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"LoganResearch/ARC-Base-8B-Condensed",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("LoganResearch/ARC-Base-8B-Condensed")
prompt = "<|im_start|>user\nExplain gradient descent briefly.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Architecture
System Overview
┌─────────────────────────────────────────────────────────────────────────────┐
│ ARC ENGINE ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ INPUT PROCESSING │ │
│ │ User Input → Command Parser → Generate / Tool Execute │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ CORE MODEL STACK │ │
│ ├─────────────────────────────────────────────────────────────────────┤ │
│ │ │ │
│ │ Base Model: Hermes-3-Llama-3.1-8B (8B parameters) │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ DENSE Adapter ─── THE CONDENSATOR trained (SFT→DPO→RL) │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ CF-HoT Heads ─── Repetition (125×), Hedging, Verbosity │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ Output Generation ─── Quality-controlled, density-optimized │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ QUALITY EVALUATION │ │
│ │ Response → Density Score → Coherence Score → Overall Quality │ │
│ │ │ │ │
│ │ ▼ │ │
│ │ ┌──────────────────────────────────────────────────────────────┐ │ │
│ │ │ Mentor Mode Check: Quality < 0.6 OR Uncertainty > 0.4? │ │ │
│ │ │ │ Yes │ │ │
│ │ │ ▼ │ │ │
│ │ │ Consult Claude → Learn from Response → Update Training Buffer │ │ │
│ │ └──────────────────────────────────────────────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ RSI EXPERIENCE BUFFER │ │
│ │ Store: prompt, response, quality, domain, difficulty, feedback │ │
│ │ │ │ │
│ │ ┌──────────┴──────────┐ │ │
│ │ ▼ ▼ │ │
│ │ Auto-Train Trigger? Dream Cycle? │ │
│ │ │ │ │ │
│ │ ▼ ▼ │ │
│ │ Micro-training Experience Replay │ │
│ │ (25 steps) (Reinforce learnings) │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ VALIDATION & COMMIT │ │
│ │ New Quality vs Old Quality → Better? COMMIT : ROLLBACK │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
RSI Loop (Recursive Self-Improvement)
┌─────────────────────────────────────────────────────────────────────────────┐
│ RECURSIVE SELF-IMPROVEMENT LOOP │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ │
│ │ CHAT │◄─────────────────────────────────────────────────┐ │
│ └────┬────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌─────────┐ │ │
│ │ MEASURE │ Calculate quality, density, coherence │ │
│ └────┬────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌─────────┐ │ │
│ │ BUFFER │ Store in experience buffer with metadata │ │
│ └────┬────┘ │ │
│ │ │ │
│ ▼ │ │
│ ┌──────────────┐ │ │
│ │ AUTO-TRIGGER │ Buffer full? Quality threshold? Feedback? │ │
│ └──────┬───────┘ │ │
│ │ │ │
│ Yes │ No ─────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ MICRO-TRAIN │ 25 steps on high-quality buffer samples │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ VALIDATE │ Compare new model vs checkpoint │
│ └──────┬──────┘ │
│ │ │
│ ┌────┴────┐ │
│ │ │ │
│ Better? Worse? │
│ │ │ │
│ ▼ ▼ │
│ COMMIT ROLLBACK │
│ │ │ │
│ └────┬────┘ │
│ │ │
│ ▼ │
│ Continue ─────────────────────────────────────────────────────────────────┘
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Mentor Mode Flow
┌─────────────────────────────────────────────────────────────────────────────┐
│ MENTOR MODE LEARNING FLOW │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ User Prompt │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Local Generation │ Generate response with local 8B model │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Quality Check │ Evaluate density, coherence, quality │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────┐ │
│ │ Quality < 0.6 OR Uncertainty > 0.4 │ │
│ └────────┬───────────────────────────┘ │
│ │ │
│ Yes │ No ──────────► Return local response │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Consult Claude │ Via API │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Create DPO Pair │ │
│ │ chosen: Claude │ │
│ │ rejected: Local │ │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Add to Buffer │ High-quality experience for training │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ Return Claude's response + log learning │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Core Technology
1. CF-HoT: Control-Field Holonomy
Predictive control through hidden-state monitoring. Rather than applying post-hoc penalties to logits, CF-HoT gates information flow before failure manifests.
┌─────────────────────────────────────────────────────────────────────────────┐
│ CF-HoT ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ Hidden States (Layers 16-24) │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Fiber Projection │ Compress to d=16 per layer │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Layer Attention │ Weighted aggregation across layers │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Risk Predictor │ Binary classifier: P(unwanted_behavior) │
│ └────────┬────────┘ │
│ │ │
│ ▼ │
│ If P > threshold ──► Apply logit penalties │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Head Performance:
| Head | Separation | Description |
|---|---|---|
| Repetition | 125× | Detects impending repetitive loops |
| Hedging | 1.5× | Blocks uncertainty markers |
| Verbosity | 2.1× | Suppresses filler content |
The repetition head achieves 125× separation between positive (pre-repetition) and negative (diverse output) hidden states, enabling reliable early warning.
2. The Condensator: Dense Response Training
4-stage training pipeline:
┌─────────────────────────────────────────────────────────────────────────────┐
│ THE CONDENSATOR PIPELINE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ STAGE 1: Supervised Fine-Tuning (SFT) │
│ ───────────────────────────────────── │
│ • 847 curated dense response examples │
│ • Learning rate: 2e-5 │
│ • Epochs: 3 │
│ │
│ STAGE 2: Direct Preference Optimization (DPO) │
│ ───────────────────────────────────────────── │
│ • Preference pairs: dense (chosen) vs verbose (rejected) │
│ • Beta: 0.1 │
│ • Epochs: 2 │
│ │
│ STAGE 3: Reinforcement Learning (PPO) │
│ ───────────────────────────────────── │
│ • Reward = quality_score - length_penalty │
│ • Conservative KL constraint │
│ • Learning rate: 1e-6 │
│ │
│ STAGE 4: Checkpointing │
│ ───────────────────── │
│ • Save every 25 steps │
│ • A/B comparison on held-out prompts │
│ • Automatic rollback if quality drops │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
3. Enhanced CF-HoT Parameters
| Parameter | Value | Reason |
|---|---|---|
| EMA Momentum | 0.995 | Stable control field |
| Gate Temperature | 2.0 | Softer sigmoid |
| Gate Bounds | [0.1, 0.9] | Prevent saturation |
| Monitoring | Every 50 steps | Detect drift |
| Warmup | 500 steps | Smooth initialization |
Command Reference
Core Commands
| Command | Description |
|---|---|
status |
System status overview |
help |
Full command menu |
help <topic> |
Topic-specific help |
quit |
Exit |
Self-Improvement
| Command | Description |
|---|---|
!improve |
Run improvement iteration |
!eval |
Full evaluation |
!train <steps> |
Training steps |
!compare |
Compare checkpoints |
!rollback |
Revert to best checkpoint |
!load <path> |
Load checkpoint |
!benchmark |
Evaluation suite |
Mentor Mode
| Command | Description |
|---|---|
!mentor |
Show mentor mode status |
!mentor on |
Enable auto-consultation |
!mentor off |
Disable mentor mode |
!mentor ask <question> |
Ask Claude and learn from response |
!mentor learn |
Show collected learnings |
RSI (Recursive Self-Improvement)
| Command | Description |
|---|---|
!auto_train on |
Enable learning during chat |
!auto_train off |
Disable auto-training |
!skills |
Quality per domain |
!forgetting |
Detect catastrophic forgetting |
!dream |
Force experience replay |
!buffer |
Experience buffer stats |
!selfplay <N> |
Run N self-play iterations |
Condensator
| Command | Description |
|---|---|
!condensator |
Run full SFT→DPO→RL pipeline |
!dpo |
Run DPO stage only |
!rl |
Run RL stage only |
!train_cfhot |
Train CF-HoT heads |
CF-HoT Control
| Command | Description |
|---|---|
!cfhot / !125x |
Toggle 125× head |
!cfhot status |
Head status |
!gate_stats |
CF-HoT gate health |
Generation Modes
| Command | Description |
|---|---|
!book |
Toggle book mode (16K tokens) |
!write <topic> |
Write extended content |
!claude <prompt> |
Direct Claude API prompt |
Tools
| Command | Description |
|---|---|
!shell <cmd> |
Execute shell command |
!python <code> |
Execute Python |
!read <path> |
Read file |
!write <path> <content> |
Write file |
!search <query> |
Web search |
!fetch <url> |
Fetch URL content |
Browser (requires Playwright)
| Command | Description |
|---|---|
!browse <url> |
Open URL |
!click <selector> |
Click element |
!type <text> |
Type text |
!read |
Read page content |
Multimedia (optional dependencies)
| Command | Description |
|---|---|
!stream |
Open live token window |
!audio / !tts |
Toggle text-to-speech |
!imagine <prompt> |
Generate image (SDXL) |
!dalle <prompt> |
Generate image (DALL-E 3) |
Experimental Features
| Command | Description |
|---|---|
!content blog <topic> |
Generate blog post |
!content youtube <topic> |
Generate video script |
Evaluation
Qualitative Comparison
| Prompt | Base Hermes-3 | ARC-Condensed |
|---|---|---|
| "hello" | "Hello! I'm here to help you with any questions or tasks you might have. Feel free to ask me anything!" (23 tokens) | "Hello. How can I help?" (5 tokens) |
| "What is recursion?" | "That's a great question! Recursion is a programming concept where a function calls itself..." (150+ tokens) | "Function calling itself until base case. Stack frames accumulate, unwind on return." (12 tokens) |
| "How are you?" | "As an AI, I don't have feelings in the traditional sense, but I'm functioning well..." (25 tokens) | "Functional. Task?" (3 tokens) |
Quantitative Metrics
| Metric | Base Model | ARC-Condensed | Change |
|---|---|---|---|
| Avg. Response Length | 150 tokens | 45 tokens | -70% |
| Filler Phrases | Present | Minimal | ~-95% |
| Information Density | 17.0 | 45.2 | +166% |
| Quality Score (internal) | 0.52 | 0.78 | +50% |
Note: These are heuristic metrics from internal evaluation. Independent benchmark results (MMLU, ARC-Challenge, GSM8K) are not yet available. We welcome independent evaluation.
Self-Improvement Trajectory (Observed)
Iteration 0: Quality 0.52 (baseline)
Iteration 5: Quality 0.68 (+31%)
Iteration 10: Quality 0.75 (+44%)
Iteration 15: Quality 0.78 (+50%, plateau)
Self-improvement shows diminishing returns after ~15 iterations. This is expected behavior, not a limitation to work around.
Installation
Minimal Installation
pip install torch transformers accelerate peft bitsandbytes datasets trl
Full Installation
pip install -r requirements.txt
Optional Dependencies
# Browser automation
pip install playwright && playwright install firefox
# Image generation
pip install diffusers pillow
# Text-to-speech
pip install pyttsx3 gTTS pygame
# Claude API (for mentor mode)
pip install anthropic
# OpenAI API (for DALL-E)
pip install openai
# Web search
pip install requests
Environment Variables
# Optional - for enhanced features
export ANTHROPIC_API_KEY="sk-ant-..." # Mentor Mode
export OPENAI_API_KEY="sk-..." # DALL-E
Configuration
Main Configuration
class Config:
# Generation
temperature = 0.85
top_p = 0.9
max_new_tokens = 512
repetition_penalty = 1.1
# CF-HoT
use_cfhot = True
use_cfhot_125x = False
cfhot_repetition_threshold = 0.6
cfhot_repetition_penalty = 6.0
# Self-improvement
min_quality_score = 0.5
target_quality_score = 0.75
training_steps_per_iteration = 25
quality_drop_threshold = 0.1
RSI Configuration
@dataclass
class RSIConfig:
auto_train_enabled: bool = False
buffer_size: int = 1000
min_experiences_to_train: int = 50
quality_threshold_for_training: float = 0.7
dream_cycle_interval: int = 100
forgetting_check_interval: int = 50
Mentor Configuration
@dataclass
class MentorConfig:
enabled: bool = False
auto_consult_threshold: float = 0.6
uncertainty_threshold: float = 0.4
learn_from_responses: bool = True
Repository Structure
ARC-Base-8B-Condensed/
│
├── arc_engine_v29_full.py # Main engine
├── README.md # This file
├── requirements.txt # Dependencies
│
├── model-00001-of-00004.safetensors # Model weights
├── model-00002-of-00004.safetensors
├── model-00003-of-00004.safetensors
├── model-00004-of-00004.safetensors
├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── special_tokens_map.json
├── generation_config.json
│
├── dense_checkpoints/ # Training checkpoints
│ └── step_*/
│
├── cfhot_checkpoints/ # CF-HoT heads
│ └── final_6000/
│ └── risk_predictor.pt
│
├── improvement_logs/ # RSI logs
└── exports/ # Checkpoint exports
Hardware Requirements
| Component | Minimum | Recommended |
|---|---|---|
| GPU VRAM | 16 GB | 24+ GB |
| System RAM | 32 GB | 64 GB |
| Storage | 50 GB | 100 GB |
| Python | 3.10+ | 3.11 |
Tested Configurations:
- NVIDIA RTX 3090 (24GB), 64GB RAM ✓
- NVIDIA RTX 4090 (24GB), 128GB RAM ✓
- NVIDIA A100 (40GB) ✓
Performance Estimates:
- Inference: ~15-25 tokens/second
- Full Condensator pipeline: ~4 hours (RTX 3090)
- Self-improvement iteration: ~30 minutes
Training From Scratch
Automated Training
python arc_engine_v29_full.py
> !condensator
This runs:
- SFT (3 epochs)
- DPO (2 epochs)
- RL (300 steps)
- Checkpoint validation
Manual Training
Step 1: Train CF-HoT Heads
> !train_cfhot
Step 2: Run Condensator
> !condensator
Step 3: Self-Improvement
> !selfplay 1000
API Reference
Start Server
> !api
[api] Server running on http://0.0.0.0:8080
Endpoints
POST /generate
curl -X POST http://localhost:8080/generate \
-H "Content-Type: application/json" \
-d '{"prompt": "What is recursion?"}'
Response:
{
"response": "Function calling itself until base case.",
"quality": 0.82,
"density": 48.3,
"tokens": 8
}
GET /health
curl http://localhost:8080/health
Limitations
Known Limitations
| Limitation | Description |
|---|---|
| Scale | Tested on 8B parameters only; scaling behavior unknown |
| Language | English only |
| Benchmarks | No formal benchmark results (MMLU, GSM8K, etc.) |
| Terseness | May be too concise for applications requiring elaboration |
| Iterations | Self-improvement plateaus after ~15 iterations |
| Memory | Full features require 16GB+ VRAM |
What This Is Not
- This is not AGI or a path to AGI
- This is not a production-ready system
- Self-improvement is bounded and reversible
- The model requires human oversight
- Claims are not independently validated
Ethical Considerations
Safety Measures
- Quality gates: All self-modification requires quality validation
- Automatic rollback: Degradation triggers checkpoint restoration
- Bounded improvement: No unbounded recursive self-modification
- Human oversight: System designed for interactive use, not autonomy
Potential Risks
- Dense responses may omit important caveats or safety information
- Self-improvement research requires careful monitoring
- Model inherits biases from base Hermes-3 and training data
- Experimental features should not be used for consequential decisions
Explicit Non-Goals
This system is not designed for:
- Autonomous operation without human oversight
- Self-replication or self-preservation
- Deception or manipulation
- Capability acquisition beyond defined scope
Technical Specification
Full technical documentation is available:
Primary Reference (Master Book):
Controlled Language Models: Decode-Time Behavioral Control and Token EfficiencyRelated Preprints:
The specification covers:
- Multi-loop training architecture
- Control field theory and implementation
- Tokenization co-evolution (fourth loop)
- Reliability engineering and rollback protocols
- Reproducibility requirements
Changelog
v2.9 (Current)
- Stealth web browser for research
- Improved training functions
- Bug fixes for selfplay training loop
v2.8
- Full RSI continuous learning system
- Auto-train during chat
- Dream cycles for experience replay
- Domain-specific skill tracking
- Catastrophic forgetting detection
v2.4
- Mentor Mode: Learn from Claude API
- Content generation tools
- Smart help system
v2.2
- Full CONDENSATOR pipeline
- Enhanced CF-HoT with EMA, gate temperature
- DPO and RL training stages
v2.0
- Initial release
- CF-HoT 125× repetition head
- Dense response training
- Basic self-improvement loop
Citation
@software{napolitano2025arc,
author = {Napolitano, Logan Matthew},
title = {{ARC-Base-8B-Condensed}: Adaptive Recursive Cognition for Self-Stabilizing Language Models},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/LoganResearch/ARC-Base-8B-Condensed},
note = {Technical specification available on Zenodo},
license = {CC BY 4.0}
}
@article{napolitano2025controlled,
author = {Napolitano, Logan Matthew},
title = {Controlled Language Models: Decode-Time Behavioral Control and Token Efficiency},
year = {2025},
doi = {10.5281/zenodo.18344021},
url = {https://zenodo.org/records/18344021},
publisher = {Zenodo},
note = {Primary technical reference for ARC-Base-8B-Condensed}
}
@article{napolitano2025controlfield,
author = {Napolitano, Logan Matthew},
title = {From Explicit Holonomy to Latent Control Fields},
year = {2025},
doi = {10.5281/zenodo.14707164},
url = {https://zenodo.org/records/14707164},
publisher = {Zenodo}
}
References
- Zou, A., et al. (2023). Representation Engineering: A Top-Down Approach to AI Transparency. arXiv:2310.01405
- Rafailov, R., et al. (2023). Direct Preference Optimization. arXiv:2305.18290
- Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
- Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. NeurIPS.
Acknowledgments
- NousResearch for Hermes-3-Llama-3.1-8B base model
- Meta AI for Llama 3.1 architecture
- Hugging Face for transformers, PEFT, TRL
- Anthropic for Claude API (Mentor Mode)
License
This work is licensed under CC BY 4.0 (Creative Commons Attribution 4.0 International).
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Contact: GitHub Issues | Hugging Face Discussions
Version: 2.9 | Last Updated: January 2025