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metadata
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: 0.38
            name: 300 samples, greedy decoding
            verified: false

Santacoder finetuned on The-Stack-dedup (GLSL subset) for 1000 steps with a batch size of 2 and full sequence length of 2048. adapted finetuning script found here

Finetuning parameters

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, which reached 0.380 with 300 samples.

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