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Model: HarleyCooper/Qwen3-0.6B-Dakota-Grammar-RL-400 Source: Original Platform
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-0.6B
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tags:
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- reinforcement-learning
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- rl
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- dakota-language
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- grammar
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- composition-rewards
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- non-coding
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- qualitative-tasks
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- grpo
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- prime-intellect
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- verifiers
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language:
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- en
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- dak
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pipeline_tag: text-generation
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---
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# Qwen3-0.6B-Dakota-Grammar-RL-400
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<div align="center">
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<img src="https://raw.githubusercontent.com/HarleyCoops/Dakota1890/main/Public/Prepositions.jpg" alt="Dakota Prepositions - High Detail Scan" style="width: 100%; max-width: 1200px; height: auto; border-radius: 8px;">
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*Exceptional level of detail preserved from the 1890 source material — every character, accent, and linguistic nuance captured with precision*
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</div>
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## Model Description
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This model is a reinforcement learning (RL) fine-tuned version of `Qwen/Qwen3-0.6B`, trained specifically for Dakota language grammar and translation tasks using **GRPO (Group Relative Policy Optimization) with compositional reward functions on qualitative linguistic tasks**.
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**GRPO is effective for linguistic-structure learning when qualitative goals are expressed as verifiable, compositional rewards.**
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### Key Features
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- **GRPO for Linguistic Structure**: GRPO is effective for linguistic-structure learning when qualitative goals are expressed as verifiable, compositional rewards
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- **Compositional Rewards**: Multi-component reward function combining character preservation (40%), morphological accuracy (40%), and semantic correctness (20%)
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- **Rapid Learning**: 150.3% improvement in 400 steps, with 90% of improvement achieved in just 21% of training
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- **Dakota Language Focus**: Trained on 5,657 grammar tasks extracted from the 1890 Dakota-English Dictionary
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- **Special Character Preservation**: Maintains Dakota orthography (ć, š, ŋ, ḣ, ṡ, á, é, í, ó, ú, etc.)
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- **Stable Training**: Low unmasked KL (0.092) demonstrates no catastrophic forgetting
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<div align="center">
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**Complete project repository with all code, data, and training traces:**
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[https://github.com/HarleyCoops/Dakota1890](https://github.com/HarleyCoops/Dakota1890)
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</div>
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## Training Details
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### Training Data
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- **Source**: 1890 Dakota-English Dictionary grammar section (pages 31-92)
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- **Tasks**: 5,657 training tasks covering:
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- Morphology (affix application, word formation)
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- Translation (Dakota ↔ English)
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- Reverse translation
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- Syntax (sentence structure)
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- Pattern identification
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- **Difficulty Levels**: Easy (1,998), Medium (2,155), Hard (398), Advanced (1,106)
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<div align="center" style="margin: 2rem 0;">
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<img src="https://raw.githubusercontent.com/HarleyCoops/Dakota1890/main/Public/grammar.jpg" alt="Dakota Grammar - Historical Text Detail" style="width: 100%; max-width: 1200px; height: auto; border-radius: 8px;">
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*Grammar section from the 1890 Dakota-English Dictionary showing detailed linguistic rules and interlinear text*
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</div>
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<div align="center" style="margin: 2rem 0;">
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<img src="https://raw.githubusercontent.com/HarleyCoops/Dakota1890/main/Public/dictionary2.jpg" alt="Dakota Dictionary - Historical Text Detail" style="width: 100%; max-width: 1200px; height: auto; border-radius: 8px;">
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*Dictionary entries from the 1890 source material, preserving Dakota orthography and special characters*
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</div>
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### Training Procedure
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- **Framework**: Prime Intellect RL (prime-rl)
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- **Algorithm**: GRPO (Group Relative Policy Optimization)
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- **Base Model**: Qwen/Qwen3-0.6B (small instruct model optimized for RL)
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- **Training Steps**: 400 steps (all completed)
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- **Total Samples**: 102,400 samples processed
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- **Batch Size**: 256
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- **Sequence Length**: 1,536 tokens
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- **Rollouts per Example**: 8
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- **Learning Rate**: 1e-6
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- **Checkpoint Interval**: Every 100 steps (kept 3 most recent)
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- **GPUs**:
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- Trainer: GPU 0
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- Inference: GPU 0
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### Reward Function Composition
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The model was trained using a **compositional reward function** that decomposes qualitative linguistic tasks into verifiable quantitative components:
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1. **Character Preservation (40% weight)**: Verifiable Unicode-level correctness for Dakota special characters (ć, š, ŋ, ḣ, ṡ, á, é, í, ó, ú)
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2. **Morphological Accuracy (40% weight)**: Pattern-matching against grammar rules for affix application and word formation
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3. **Semantic Correctness (20% weight)**: Meaning preservation metrics for translation quality
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**Why This Matters**: By decomposing rewards into independently verifiable components, we transform qualitative tasks (traditionally considered unsuitable for RL) into quantitatively optimizable objectives. This enables GRPO to work effectively because:
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- Each component is independently verifiable (no human judgment needed)
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- Gradients flow through each component (model learns what to prioritize)
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- Multi-dimensional feedback (model knows exactly what it got wrong)
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### Environment
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- **Environment**: `dakota_grammar_translation` (local installation)
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- **Framework**: Verifiers-compatible RL environment
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- **Parser**: DakotaTranslationParser (preserves Dakota orthography)
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|
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## Training Results
|
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|
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### Key Achievements
|
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|
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- **150.3% improvement** in overall reward (0.128 → 0.321, peak: 0.345)
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- **Rapid learning**: 90% of improvement achieved in first 85 steps (21.25% of training)
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- **Sample efficiency**: 0.000483 improvement per step - demonstrating dense learning signals
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- **Stable training**: Controlled KL divergence with unmasked KL remaining low (mean: 0.094, final: 0.092)
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- **Policy confidence**: Entropy decreased from 0.93 to 0.28, showing increased model certainty
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### Training Metrics
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- **Final Entropy**: 0.28 (mean), 0.024 (median)
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- **Inference Probabilities**: Increased throughout training
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- **Peak Memory**: 13.9 GiB
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- **KL Divergence**:
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- Masked KL: 11.96 (final) - substantial policy adaptation for Dakota-specific tokens
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- Unmasked KL: 0.092 (final) - preserved general language capabilities
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- Overall KL: 5.03 (final) - controlled policy adaptation
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### W&B Runs
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- **Project**: dakota-rl-grammar
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- **Trainer Run**: [`yut26kcm`](https://wandb.ai/christian-cooper-us/dakota-rl-grammar/runs/yut26kcm) - `dakota-0.6b-ledger-test-400-trainer`
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- **Orchestrator Run**: [`1y33h9zr`](https://wandb.ai/christian-cooper-us/dakota-rl-grammar/runs/1y33h9zr) - `dakota-0.6b-ledger-test-400-orchestrator`
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### Training Visualizations
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*Comprehensive dashboard showing reward progression, component performance, loss dynamics, entropy, and KL divergence*
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|
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*Reward progression demonstrating 150.3% improvement with 90% achieved in just 21% of training*
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|
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|
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|
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*Training metrics showing stable optimization, decreasing entropy, and controlled KL divergence*
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*Performance metrics showing consistent throughput and GPU utilization*
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## GRPO for Qualitative Tasks: Significance
|
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|
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**GRPO is effective for linguistic-structure learning when qualitative goals are expressed as verifiable, compositional rewards.** This is significant because:
|
||||
|
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### Why This Matters
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GRPO has been successfully applied to **quantitative domains** (code generation, mathematical reasoning) where correctness is verifiable and rewards are clear. However, **qualitative tasks** like language learning, translation, and grammar have traditionally been considered unsuitable for RL because:
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1. **Subjective evaluation**: "Is this translation good?" lacks clear criteria
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2. **Multi-dimensional quality**: A translation can be semantically correct but orthographically wrong
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3. **Nuanced feedback**: Binary correct/incorrect fails to capture partial correctness
|
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|
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### Our Solution: Compositional Rewards
|
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|
||||
By decomposing rewards into **linguistic primitives** (character preservation, morphological accuracy, semantic correctness), we transform qualitative tasks into **quantitatively optimizable objectives**. This decomposition enables GRPO to work effectively because each component is independently verifiable, gradients flow through each component, and the model receives multi-dimensional feedback.
|
||||
|
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### Key Results Demonstrating Significance
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||||
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1. **150.3% improvement in 400 steps** - Comparable to GRPO performance on coding tasks
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2. **90% improvement in 21% of training** - Demonstrates dense learning signals from compositional rewards
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3. **Low unmasked KL (0.092)** - Model specializes without catastrophic forgetting
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4. **Stable training dynamics** - No reward hacking or instability issues
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### Implications
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**GRPO is effective for linguistic-structure learning when qualitative goals are expressed as verifiable, compositional rewards.** When qualitative tasks are decomposed into verifiable components, they become as learnable as coding or math. This opens new possibilities for:
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- **Low-resource language learning** (this work)
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- **Style transfer** (decompose into syntax, semantics, register)
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- **Dialogue systems** (decompose into coherence, relevance, appropriateness)
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- **Creative tasks** (decompose into structure, originality, coherence)
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## Intended Use
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This model is intended for:
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- Research on GRPO for qualitative linguistic tasks
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- Demonstrating compositional reward functions in RL pipelines
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- Dakota language grammar and translation tasks
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- Testing RL effectiveness on linguistic-structure learning with compositional rewards
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- Low-resource language learning applications
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## Limitations
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- Small model size (0.6B parameters) limits capacity for complex grammar rules
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- Trained on historical dictionary data (1890) which may not reflect modern Dakota usage
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- Limited to single-turn and multi-turn chat formats
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- Requires Dakota language knowledge for proper evaluation
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- 400-step training run (test run) - longer training may yield further improvements
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## Ethical Considerations
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- Trained on historical linguistic data from indigenous language documentation
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- Should be used respectfully and in consultation with Dakota language communities
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- Not intended to replace human language experts or native speakers
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- Part of language preservation and revitalization efforts
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## Citation
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```bibtex
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@misc{dakota1890-rl-400-2024,
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title={Qwen3-0.6B-Dakota-Grammar-RL-400: GRPO for Qualitative Linguistic Tasks with Compositional Rewards},
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author={Christian H. Cooper},
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year={2024},
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url={https://huggingface.co/HarleyCooper/Qwen3-0.6B-Dakota-Grammar-RL-400},
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note={Demonstrates GRPO effectiveness on qualitative tasks through compositional reward decomposition}
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}
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```
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## Acknowledgments
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- Base model: Qwen/Qwen3-0.6B by Alibaba Cloud
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- Training framework: Prime Intellect RL (prime-rl)
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- Source material: 1890 Dakota-English Dictionary by Stephen Return Riggs
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- Environment: Dakota1890 RL environment (dakota_grammar_translation)
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- Weights & Biases: Training monitoring and visualization
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## Model Card Contact
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For questions or issues, please contact: Raise an Issue in the [Repository](https://github.com/HarleyCoops/Dakota1890)
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"</think>": 151668,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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}
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89
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
|
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
|
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{%- if '</think>' in content %}
|
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
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{%- endif %}
|
||||
{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
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{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
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||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
|
||||
{%- if message.tool_calls %}
|
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{%- for tool_call in message.tool_calls %}
|
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{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
61
config.json
Normal file
61
config.json
Normal file
@@ -0,0 +1,61 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "float32",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1024,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3072,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.56.1",
|
||||
"use_cache": false,
|
||||
"use_grouped_mm": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.56.1"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a0a127ed2d3885284d216e87de6ad47cb30d155ff020976c1b315030b02cb001
|
||||
size 1503365567
|
||||
25
special_tokens_map.json
Normal file
25
special_tokens_map.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|im_end|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|im_end|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user