Qwen2.5-Coder-3B-Ilograph-Instruct/README.md

---
base_model: unsloth/Qwen2.5-Coder-3B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen2.5
- sft
- transformers
- ilograph
license: mit
datasets:
- Brigham-Young-University/Ilograph_dataset
language:
- en
new_version: Brigham-Young-University/Qwen3-Coder-30B-A3B-Ilograph-Instruct
---

# Model Card for Qwen2.5-Coder-3B-Instruct (fine-tuned model)

A fully fine-tuned version of **Qwen2.5-Coder-3B-Instruct**, trained with LoRA using Unsloth and then merged into a standalone model. This checkpoint can be used directly as a regular Transformers causal language model. It is specialized for **Ilograph diagrams**: it generates valid **Ilograph Diagram Language (IDL)** specifications from natural-language instructions.

The repository includes a **system prompt** you can pass to the model and an **IDL schema** (JSON) that describes the expected output format; the schema is available in the repository.

## Model Details

- **Developed by:** Chris Mijangos (AI student architect at BYU)
- **Shared by:** Brigham Young University (BYU)
- **Model type:** Causal language model (decoder-only), fine-tuned Qwen2.5-Coder-3B-Instruct (trained with LoRA, merged into base weights)
- **Language(s):** Primarily English; capabilities depend on base model and fine-tuning data
- **License:** Same as base model; verify [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct) license terms before use
- **Finetuned from:** [unsloth/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-3B-Instruct)

### Model Sources

- **Repository:** This model card and weights are shared via the associated Hugging Face repo
- **Demo:** N/A — In construction 

## Uses

### Direct Use

Load the adapter with the base model to generate **Ilograph (IDL)** diagram specifications from instructions. Use the system prompt and schema in the repository (see below). Use the “How to Get Started” section below for loading the model.

### Out-of-Scope Use

This model is not intended for high-risk or safety-critical applications without further evaluation. Do not use for generating misleading, harmful, or illegal content. Users are responsible for complying with applicable laws and the base model’s license.

## Bias, Risks, and Limitations

As with other language models, this adapter may reflect biases present in the base model and in the fine-tuning data. Outputs should be validated for your use case. No formal bias or safety evaluation is provided with this release.

Due to limited and focused training data and the small size of the base model, this adapter is primarily suited for relatively simple Ilograph diagrams centered on **resources, relationships, and sequences**. For more complex, large-scale, or highly customized diagram structures, the model may not perform as well and additional fine-tuning or a larger base model may be required. I you want to have more complex diagrams you can use our Qwen3 30B newwer version. 


### Recommendations

Users should evaluate the model on their own data and tasks and be aware of potential biases and limitations before deployment.

## How to Get Started with the Model

Requires the base model and PEFT. Install dependencies:

```bash
pip install transformers peft accelerate
```

Load the fine-tuned model directly from this repository:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Brigham-Young-University/Qwen2.5-Coder-3B-Ilograph-Instruct" 

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    trust_remote_code=True,
)

inputs = tokenizer("Your prompt here", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Ilograph (IDL) system prompt and schema

The repository includes a **system prompt** and an **IDL schema** (JSON). Use the schema to fill in the placeholder in the prompt, then append your instruction. Example system prompt:

```
You are an expert in the Ilograph Diagram Language (IDL). You have been trained on data that is formatted in the following way:

<insert the schema JSON here>

Your task is to create a valid IDL specification for the diagram. You will be given an instruction of what to create, and you will need to create a valid IDL specification for the diagram.

CRITICAL RULES:
- NEVER use JSON format
- NEVER use Mermaid syntax
- NEVER use any format except ilograph YAML
- Use YAML syntax with proper indentation

Here is the instruction:
```

The schema is provided in the repository; inject its contents (e.g. as formatted JSON) where indicated above, then add your diagram instruction after “Here is the instruction:”.

## Evaluation

No formal evaluation results are provided with this release. Users are encouraged to evaluate the model on their own benchmarks and tasks.


## Model Card Authors

- Chris Mijangos (BYU)

## Model Card Contact

For questions about this model card or the adapter, please open an issue on the associated Hugging Face repository or contact through BYU

### Framework versions

- PEFT 0.18.1