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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
+tags:
+  - dyck-language
+  - bracket-completion
+  - reasoning
+  - lora
+  - fine-tuned
+task: text-generation
+language: en
+---
+
+# DeepSeek-R1-Dyck-Finetuned
+
+## Model Description
+
+This model is a fine-tuned version of **unsloth/DeepSeek-R1-Distill-Llama-8B** specifically optimized for **Dyck language bracket completion** tasks. The model has been trained to complete incomplete Dyck bracket sequences by tracking the stack of open brackets and generating the appropriate closing brackets.
+
+### Key Features
+
+- **Reasoning Capability**: Generates step-by-step reasoning using `<think>` blocks before providing the final answer
+- **Dyck Language Completion**: Accurately completes bracket sequences for 8 different bracket types: `()`, `[]`, `{}`, `<>`, `⟨⟩`, `⟦⟧`, `⦃⦄`, `⦅⦆`
+- **LoRA Fine-tuning**: Uses Low-Rank Adaptation (LoRA) for efficient training
+- **High Accuracy**: Trained on 60k diverse Dyck sequence examples
+
+## Training Details
+
+### Training Data
+- **Dataset**: 60k Dyck language sequences
+- **Train/Val Split**: 95%/5%
+- **Format**: Chat template with system/user/assistant messages
+- **Reasoning**: All samples include `<think>` reasoning blocks
+
+### Training Configuration
+- **Base Model**: unsloth/DeepSeek-R1-Distill-Llama-8B
+- **LoRA Rank**: 32 (attention layers only)
+- **LoRA Alpha**: 64
+- **LoRA Dropout**: 0.25
+- **Learning Rate**: 3e-6
+- **Batch Size**: 4 × 32 (effective batch: 128)
+- **Epochs**: 4
+- **Warmup**: 30% of total steps
+- **Gradient Clipping**: 0.05
+- **Optimizer**: AdamW
+- **Scheduler**: Linear
+
+### Training Hardware
+- **GPU**: 40GB GPU
+- **Precision**: Full (bfloat16)
+- **Training Time**: ~6-8 hours
+
+## Usage
+
+### Installation
+
+```bash
+pip install unsloth transformers
+```
+
+### Loading the Model
+
+```python
+from unsloth import FastLanguageModel
+
+# Load LoRA adapters
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="akashdutta1030/dddd",
+    max_seq_length=2048,
+    dtype=None,
+    load_in_4bit=False,  # Use True for 4-bit quantization
+)
+
+FastLanguageModel.for_inference(model)
+```
+
+### Inference Example
+
+```python
+messages = [
+    {
+        "role": "system",
+        "content": "You are a logic engine. Complete the Dyck bracket sequence by tracking the stack of open brackets."
+    },
+    {
+        "role": "user",
+        "content": "([{<"
+    }
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.1,
+        top_p=0.95,
+        do_sample=True,
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=False)
+print(response)
+```
+
+### Expected Output Format
+
+The model generates responses in the following format:
+
+```
+<think>
+1. Input sequence: (, [, {, <
+2. Maintain a stack of opening brackets:
+   - Push '(' -> Stack: ['(']
+   - Push '[' -> Stack: ['(', '[']
+   - Push '{' -> Stack: ['(', '[', '{']
+   - Push '<' -> Stack: ['(', '[', '{', '<']
+3. To close the sequence, pop from the stack in reverse order:
+   - Pop '<' -> Closing: '>'
+   - Pop '{' -> Closing: '}'
+   - Pop '[' -> Closing: ']'
+   - Pop '(' -> Closing: ')'
+4. Appending closing brackets to input: ([{<>}])
+</think>
+([{<>}])
+```
+
+## Model Architecture
+
+- **Base Architecture**: Llama-based (DeepSeek-R1)
+- **Parameters**: 8B base model
+- **LoRA Parameters**: ~167M trainable parameters (1.8% of base model)
+- **Target Modules**: Attention layers only (q_proj, k_proj, v_proj, o_proj)
+
+## Performance
+
+The model has been trained and validated on:
+- **Training Loss**: Decreasing smoothly
+- **Validation Loss**: Monitored every 200 steps
+- **Gradient Stability**: Controlled with strict clipping (0.05)
+- **Reasoning Quality**: Generates detailed step-by-step reasoning
+
+## Limitations
+
+- The model is specifically trained for Dyck language bracket completion
+- Performance may vary on sequences with very deep nesting (>20 levels)
+- Requires proper formatting with chat template for best results
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{deepseek-r1-dyck-finetuned,
+  title={DeepSeek-R1-Dyck-Finetuned: Bracket Completion Model},
+  author={Fine-tuned on DeepSeek-R1-Distill-Llama-8B},
+  year={2024},
+  howpublished={\url{https://huggingface.co/akashdutta1030/dddd}}
+}
+```
+
+## License
+
+This model is licensed under the Apache 2.0 license.
+
+## Acknowledgments
+
+- Base model: DeepSeek-R1-Distill-Llama-8B
+- Training framework: Unsloth
+- Fine-tuning approach: LoRA (Low-Rank Adaptation)