README.md

---

license: apache-2.0
tags:
- text-generation
- transformers
- safetensors
- conversational
pipeline_tag: text-generation
library_name: transformers
---

# Mysterious Coding Model

This repository contains a specialised AI model for agentic code generation and text generation tasks. The model is inspired by the GPT‑OSS series (gpt oss 20b and gpt oss 120b) described in [the corresponding paper](https://arxiv.org/abs/2508.10925). It is built on open‑source Llama architecture and fine‑tuned for programming assistance, conversation and multi‑language support.

## Key Features

- **Open source**: released under the Apache‑2.0 license.
- **Text and code generation**: supports code completion, bug fixing, refactoring and documentation generation.
- **Efficient storage**: models are stored in the secure and fast `safetensors` format.
- **Multiple precisions**: includes base FP16 models, 8‑bit quantised models and MXFP4 (mixed precision) variants.
- **vLLM compatibility**: compatible with the vLLM engine for high‑throughput inference.
- **Conversational**: instruction tuned for interactive coding assistance.

## Repository Structure

```
coding-model-repository/
├── README.md
├── .gitattributes              # Updated for safetensors
├── .gitignore
├── requirements.txt
├── model_index.json           # Safetensors model index
├── config.json                # Coding model configuration
├── model_card.md             # Coding model documentation
│
├── models/
│   ├── library=safetensors/   # Main safetensors models directory
│   │   ├── base/
│   │   │   ├── model-00001-of-00003.safetensors
│   │   │   ├── model-00002-of-00003.safetensors
│   │   │   ├── model-00003-of-00003.safetensors
│   │   │   ├── model.safetensors.index.json
│   │   │   ├── config.json
│   │   │   ├── generation_config.json
│   │   │   └── tokenizer/
│   │   │       ├── tokenizer.json
│   │   │       ├── tokenizer_config.json
│   │   │       ├── vocab.json
│   │   │       ├── merges.txt
│   │   │       └── special_tokens_map.json
│   │   │
│   │   ├── quantized/
│   │   │   ├── 4bit/
│   │   │   │   ├── model.safetensors
│   │   │   │   └── quantization_config.json
│   │   │   ├── 8bit/
│   │   │   │   ├── model.safetensors
│   │   │   │   └── quantization_config.json
│   │   │   └── awq/
│   │   │       ├── model.safetensors
│   │   │       └── quant_config.json
│   │   │
│   │   ├── instruct/
│   │   │   ├── model.safetensors
│   │   │   ├── config.json
│   │   │   └── training_config.json
│   │   │
│   │   └── specialized/
│   │       ├── python-focused/
│   │       │   └── model.safetensors
│   │       ├── web-dev/
│   │       │   └── model.safetensors
│   │       ├── systems-programming/
│   │       │   └── model.safetensors
│   │       └── data-science/
│   │           └── model.safetensors
│   │
│   ├── adapters/              # Safetensors adapters
│   │   ├── lora/
│   │   │   ├── adapter_model.safetensors
│   │   │   └── adapter_config.json
│   │   ├── coding-specific/
│   │   │   ├── debugging-adapter.safetensors
│   │   │   ├── refactoring-adapter.safetensors
│   │   │   └── documentation-adapter.safetensors
│   │   └── language-specific/
│   │       ├── python-adapter.safetensors
│   │       ├── javascript-adapter.safetensors
│   │       ├── rust-adapter.safetensors
│   │       └── cpp-adapter.safetensors
│   │
│   └── merged/                # Merged coding models
│       ├── code-instruct-merge/
│       │   └── model.safetensors
│       ├── multilang-merge/
│       │   └── model.safetensors
│       └── merge_recipes/
│           ├── coding_merge_v1.json
│           └── instruct_coding_merge.json
│
├── datasets/                  # Coding datasets
│   ├── training/
│   │   ├── code_samples/
│   │   ├── documentation/
│   │   └── problem_solutions/
│   ├── evaluation/
│   │   ├── humaneval/
│   │   ├── mbpp/
│   │   ├── codecontests/
│   │   └── custom_benchmarks/
│   └── instruction_tuning/
│       ├── code_alpaca/
│       ├── evol_instruct_code/
│       └── magicoder_data/
│
├── scripts/
│   ├── convert_to_safetensors.py    # Convert models to safetensors
│   ├── validate_safetensors.py     # Validate safetensors integrity
│   ├── quantize_coding_model.py    # Code-optimized quantization
│   ├── merge_coding_models.py      # Merge coding-specific models
│   ├── train_coding_adapter.py     # Train coding adapters
│   ├── evaluate_coding.py          # Code generation evaluation
│   └── benchmark_performance.py    # Performance benchmarks
│
├── evaluation/
│   ├── code_generation/
│   │   ├── python_eval.py
│   │   ├── javascript_eval.py
│   │   └── multilang_eval.py
│   ├── code_completion/
│   │   ├── completion_benchmark.py
│   │   └── context_accuracy.py
│   ├── code_understanding/
│   │   ├── bug_detection.py
│   │   ├── code_explanation.py
│   │   └── refactoring_suggestions.py
│   └── benchmarks/
│       ├── humaneval_results/
│       ├── mbpp_results/
│       └── custom_results/
│
├── tools/
│   ├── code_formatter.py
│   ├── syntax_validator.py
│   ├── dependency_analyzer.py
│   └── performance_profiler.py
│
└── docs/
    ├── coding_model_guide.md
    ├── safetensors_usage.md
    ├── evaluation_metrics.md
    └── api_reference.md
```

## Usage

To load the model and generate code using `transformers` and `safetensors`, run:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the safetensors model
auto_model = AutoModelForCausalLM.from_pretrained(
    "likhonhfai/mysterious-coding-model",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained("likhonhfai/mysterious-coding-model")

prompt = "def fibonacci(n):\n    \"\"\"Calculate the nth Fibonacci number\"\"\"\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = auto_model.generate(
    **inputs,
    max_new_tokens=64,
    do_sample=True,
    top_p=0.95,
    temperature=0.1
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

For vLLM-based inference or to use quantized models (4‑bit, 8‑bit or AWQ), explore the subdirectories under `models/quantized/` and see the scripts for quantisation and evaluation.

## Safetensors Format

All model weights are stored in `.safetensors` format. This binary format provides:

1. **Security** – loading the model doesn’t execute arbitrary code.
2. **Speed** – faster loading compared to pickle-based formats.
3. **Memory efficiency** – supports lazy loading.
4. **Cross-platform compatibility** – works across operating systems.
5. **Rich metadata** – makes it easier to inspect and validate model shards.

Refer to `scripts/convert_to_safetensors.py` to convert PyTorch checkpoints into safetensors.

## Quantisation

The `models/quantized/` directory contains 4‑bit, 8‑bit and AWQ quantised versions of the model. These variants reduce memory requirements and accelerate inference with minimal impact on accuracy. See `scripts/quantize_coding_model.py` for details.

## Evaluation

Benchmark scripts are available under `evaluation/` and `scripts/evaluate_coding.py`. Use them to run HumanEval, MBPP and other coding benchmarks. Example:

```bash
python scripts/evaluate_coding.py --benchmark humaneval
```

## ArXiv Reference

This model draws on techniques described in the paper ["gpt oss 120b & gpt oss 20b"](https://arxiv.org/abs/2508.10925), which details the training and capabilities of open‑source GPT‑OSS models.

## Contribution

Contributions are welcome! Feel free to open issues or pull requests to improve the code, documentation, or add new adapters and datasets.