---
language:
- code
license: bigcode-openrail-m
datasets:
- bigcode/the-stack-dedup
pipeline_tag: text-generation
tags:
- code
- shader
base_model: bigcode/santacoder
widget:
- text: void mainImage( out vec4 fragColor, in vec2 fragCoord )
  example_title: mainImage
  group: Shadertoy
model-index:
- name: santacoder-finetuned-the-stack-glsl
  results:
  - task:
      type: text-generation
      name: ShaderEval
    dataset:
      type: Vipitis/Shadertoys-fine
      name: Shadertoys-fine
      config: return_completion
      revision: 0.0.2
    metrics:
      - type: exact_match
        value: 0.380
        name: 300 samples, greedy decoding
        verified: false
---

[Santacoder](https://huggingface.co/bigcode/santacoder) finetuned on [The-Stack-dedup (GLSL subset)](https://huggingface.co/datasets/bigcode/the-stack-dedup/tree/main/data/glsl) for 1000 steps with a batch size of 2 and full sequence length of 2048.
adapted finetuning script found [here](./train.py)

### Finetuning parameters
```sh
python3 train.py --model_path "bigcode/santacoder" \
--dataset_name "bigcode/the-stack-dedup" \
--subset "data/glsl" \
--data_column "content" \
--split "train" \
--seq_length 2048 \
--max_steps 1000 \
--batch_size 2 \
--gradient_accumulation_steps 4 \
--learning_rate 5e-5 \
--num_warmup_steps 100 \
--eval_freq 100 \
--save_freq 100 \
--log_freq 1 \
--output_dir "checkpoint_dir" \
--no_fp16

```

Main purpose of this model is to explore if finetuning models improves performance on [ShaderEval](https://huggingface.co/spaces/Vipitis/ShaderEval), which reached 0.380 with 300 samples.

License carried over from model, and the finetuning dataset holds the same license.