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---
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.
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