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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B
+tags:
+  - dyck
+  - reasoning
+  - brackets
+  - fine-tuning
+  - lora
+  - unsloth
+language:
+  - en
+datasets:
+  - conversation.jsonl
+pipeline_tag: text-generation
+---
+
+# Dyck Completion Model (Reasoning)
+
+This model is fine-tuned to **complete Dyck sequences** (balanced bracket sequences) with **step-by-step reasoning**. Given a prefix of opening brackets, it outputs the minimal closing brackets so the full sequence is a valid Dyck word.
+
+**Response style:** Output follows the **dataset format only** (structured `# Thought N:`, `# Step k: add 'X'.`, then `FINAL ANSWER: <sequence>`). It is not intended to mimic Qwen/DeepSeek-style prose (e.g. no "Wait...", "Let me recount", or conversational commentary). Training and inference prompts enforce this dataset style.
+
+## Task
+
+- **Input:** A prefix of opening brackets (e.g. `[ < (`).
+- **Output:** Step-by-step reasoning, then the **complete valid Dyck sequence** (e.g. `) > ]` appended).
+- **Bracket pairs:** `()`, `[]`, `{}`, `<>`
+
+## Base Model
+
+- **Architecture:** [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Qwen-1.5B) (Unsloth)
+- **Fine-tuning:** LoRA (r=64, alpha=128, dropout=0.05) on q/k/v/o and MLP projections
+- **Training:** Causal LM; loss on assistant tokens only; format: `{reasoning}\n\nFINAL ANSWER: {full_sequence}`
+
+## Intended Use
+
+- Research and education on formal language (Dyck) and chain-of-thought reasoning.
+- Benchmarking reasoning models on bracket completion.
+
+## How to Use
+
+**Inference:** Use the **merged model** (single load, base+LoRA already merged) or load base + adapter via PEFT. Merged model = one `AutoModelForCausalLM`; computation is equivalent to base+adapter at every layer.
+
+### With merged model (this repo, if uploaded as merged)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "YOUR_USERNAME/YOUR_REPO"  # e.g. akashdutta1030/dyck-deepseek-r1-lora
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+prompt = """Complete the following Dyck language sequence by adding the minimal necessary closing brackets.
+
+Sequence: [ < (
+
+Rules:
+- Add only the closing brackets needed to match all unmatched opening brackets
+- Response format (dataset style only): Use "# Thought N: ..." for each step, then "# Step k: add 'X'.", then "FINAL ANSWER: " followed by the complete Dyck sequence. Do not add Qwen/DeepSeek-style prose or conversational commentary."""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.05)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Parse "FINAL ANSWER: ..." from response for the completed sequence
+```
+
+### With LoRA adapter (load base + adapter)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/DeepSeek-R1-Distill-Qwen-1.5B",
+    max_seq_length=768,
+)
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "YOUR_USERNAME/YOUR_REPO",  # adapter repo
+    max_seq_length=768,
+)
+# Then generate as above
+```
+
+## Training Details
+
+- **Data:** JSONL conversations (user question → assistant reasoning + final answer). Dataset size configurable (e.g. 60k).
+- **Split:** ~95% train, ~5% eval.
+- **Sequence length:** 768 tokens (run `check_dataset_seq_len.py` to confirm max).
+- **Optimization:** AdamW, cosine LR 6e-6, warmup 25%, max_grad_norm=0.5. 2 epochs typical.
+- **Weighted loss:** Tokens from "FINAL ANSWER: " onward get weight 5.0; reasoning tokens 1.0 (stronger signal on the answer).
+
+## Limitations
+
+- Trained on synthetic Dyck data; may not generalize to arbitrary bracket-like tasks.
+- Performance depends on prefix length and bracket vocabulary.
+
+## Citation
+
+If you use this model, please cite the base model (DeepSeek-R1-Distill-Qwen) and this fine-tuning setup as appropriate.