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 (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)

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)

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.