README.md

---
library_name: transformers
license: mit
base_model: microsoft/DialoGPT-small
tags:
  - generated_from_trainer
  - dialog
  - chess
  - gpt-2
model-index:
  - name: dialochess
    results: []
---
# Model card: **dialochess**

**Short description**  
`dialochess` is a conversational model derived from `microsoft/DialoGPT-small` and fine-tuned for chess-related dialog and play. It was trained using the dataset referenced by the trainer (see *Training data* below) and configured to interact in chess play/analysis settings such as the Hugging Face Space `mlabonne/chessllm`.

---

## Model details

- **Model type:** GPT-2 / DialoGPT-small family (causal, autoregressive)
- **Base model:** `microsoft/DialoGPT-small`
- **Fine-tuned name:** `dialochess`
- **License:** MIT
- **Libraries & versions used during training (reported by trainer):**
  - Transformers 4.52.4
  - PyTorch 2.7.1+cu118
  - Datasets 3.6.0
  - Tokenizers 0.21.1

---

## Model description

`dialochess` is a fine-tuned conversational transformer designed to generate chess-specific dialogue, including move suggestions, commentary, brief positional analyses, and short games against other AIs. While it remains an autoregressive language model (not a dedicated chess engine), it can produce text tokens encompassing algebraic moves, evaluation phrases, and natural-language explanations.

After analyzing the training and performance of several models, it was found that DialoGPT can achieve a much higher level of conversational fluency and contextual understanding than its original GPT-2 base. This makes `dialochess` capable of generating more coherent, context-aware, and chess-relevant responses.


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## Intended uses & limitations

**Intended uses**
- Research and experimentation in conversational chess agents.
- Integration into chat-based chess interfaces for move suggestions and commentary.
- Generating sample game dialogues or annotated move lists for educational/demo purposes.
- Fine-tuning baseline for further chess-specific language-model work.

**Not suitable for**
- Replacing a dedicated chess engine for precise tactical calculation (e.g., Stockfish, Leela).
- High-stakes or competitive play where rigorous move correctness and deep search are required.
- Any medical, legal, financial, or safety-critical advice — it's a domain-specific conversational model and may hallucinate or produce incorrect information.

**Limitations**
- May hallucinate moves, annotations, or claims about positions.
- Performance is dependent on the quality and diversity of the fine-tuning dataset (see *Training and evaluation data*).
- No official evaluation metrics were included in the automatically-generated card. Users should validate with specific benchmarks (perplexity, move-accuracy, Elo/win-rate against baselines).

---

## Training and evaluation data

- **Dataset (provided by the trainer):** The fine-tuning dataset referenced in the trainer materials and available in the provided Colab notebook:  
  `https://colab.research.google.com/drive/11UjbfajCzphe707_V7PD-2e5WIzyintf`  
  (This link was included by the trainer. Please review the Colab to inspect dataset sources, composition, license, and any preprocessing steps.)
- **Source / provenance:** The model was trained to interact with or play against other AIs in the Hugging Face Space `mlabonne/chessllm`. See the space here: `https://huggingface.co/spaces/mlabonne/chessllm`.
- **Data filtering & cleaning:** Not provided in the auto-generated metadata. It is recommended to include details about tokenization choices, any filtering of illegal moves or metadata removal, and train/validation splits.
- **Privacy & licenses:** The original trainer metadata did not list dataset license(s). Verify that any third-party game logs, PGN files, or scraped content used for training are permitted under their licenses before public redistribution.

---

## Training procedure

**Hyperparameters (as reported):**
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: AdamW (betas=(0.9, 0.999), eps=1e-08)
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP

**Notes**
- The model was fine-tuned from `microsoft/DialoGPT-small`. The training ran for 1 epoch (per provided metadata). For improved performance, consider longer training, larger datasets, and careful evaluation/early stopping.
- No detailed training logs or evaluation metrics were included in the auto-generated card; add validation loss curves, perplexity, and any chess-specific metrics (move prediction accuracy, legality rate, win-rate vs baseline) to the card if available.

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## Evaluation & recommended metrics

No evaluation results were included in the auto-generated card. To assess quality, we recommend reporting:
- **Perplexity** on a held-out validation set.
- **Move accuracy**: fraction of model-predicted moves that match the recorded moves in a test PGN corpus.
- **Legal-move rate**: fraction of generated moves that are legal given the position.
- **Win-rate / Elo proxy**: Play matches against a fixed baseline agent and report win/draw/loss and Elo-like estimates.
- **Human preference / qualitative eval**: human raters judge helpfulness, fluency, and chess correctness in dialog samples.

If you want, run a small evaluation pipeline and paste the results here so this section can be updated.

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## Safety & biases

- The model can generate incorrect or misleading chess content. Verify generated moves with a chess engine before acting on them.
- As an autoregressive language model, it may reproduce biases or toxic language present in the training data. Use standard moderation / filtering if deploying publicly.
- Avoid exposing the model as a canonical authority on chess positions or instructing users to rely on it without verification.

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