
[Santacoder](https://huggingface.co/bigcode/santacoder) finetuned on [Shadertoys](https://huggingface.co/datasets/Vipitis/Shadertoys) for 1000 steps with a batch size of 2 and full sequence length of 2048.
adapted finetuning script found [here](./train.py)

Try model in the [ShaderCoder](https://huggingface.co/spaces/Vipitis/ShaderCoder) demo space

### Finetuning parameters
```sh
python3 train.py --model_path "bigcode/santacoder" \
--dataset_name "Vipitis/Shadertoys" \
--data_column "code" \
--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 0.550 with 300 samples.

### Disclaimer

While the train/test split is held out, there is a lot of data contamination. The model results can't be trusted for this simple benchmark.
Better tasks for the benchmark will be developed and tested against these models.

License carried over from model, however training data has an undefied license. Check details in [Shadertoys](https://huggingface.co/datasets/Vipitis/Shadertoys).
{"language": ["code"], "license": "bigcode-openrail-m", "tags": ["code", "shader"], "datasets": ["bigcode/the-stack-dedup", "Vipitis/Shadertoys"], "pipeline_tag": "text-generation", "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": {"name": "Shadertoys-fine", "type": "Vipitis/Shadertoys-fine", "config": "return_completion", "revision": "0.0.2"}, "metrics": [{"type": "exact_match", "value": 0.55, "name": "300 samples, greedy decoding", "verified": false}]}]}]}
transformers pytorch safetensors gpt2 text-generation code shader custom_code dataset:bigcode/the-stack-dedup dataset:Vipitis/Shadertoys base_model:bigcode/santacoder license:bigcode-openrail-m model-index autotrain_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code #shader #custom_code #dataset-bigcode/the-stack-dedup #dataset-Vipitis/Shadertoys #base_model-bigcode/santacoder #license-bigcode-openrail-m #model-index #autotrain_compatible #has_space #text-generation-inference #region-us 


Santacoder finetuned on Shadertoys for 1000 steps with a batch size of 2 and full sequence length of 2048.
adapted finetuning script found here

Try model in the ShaderCoder demo space

### Finetuning parameters


Main purpose of this model is to explore if finetuning models improves performance on ShaderEval, which reached 0.550 with 300 samples.

### Disclaimer

While the train/test split is held out, there is a lot of data contamination. The model results can't be trusted for this simple benchmark.
Better tasks for the benchmark will be developed and tested against these models.

License carried over from model, however training data has an undefied license. Check details in Shadertoys.
### Finetuning parameters


Main purpose of this model is to explore if finetuning models improves performance on ShaderEval, which reached 0.550 with 300 samples. ### Disclaimer

While the train/test split is held out, there is a lot of data contamination. The model results can't be trusted for this simple benchmark.
Better tasks for the benchmark will be developed and tested against these models.

License carried over from model, however training data has an undefied license. Check details in Shadertoys.
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code #shader #custom_code #dataset-bigcode/the-stack-dedup #dataset-Vipitis/Shadertoys #base_model-bigcode/santacoder #license-bigcode-openrail-m #model-index #autotrain_compatible #has_space #text-generation-inference #region-us 
 ### Finetuning parameters


Main purpose of this model is to explore if finetuning models improves performance on ShaderEval, which reached 0.550 with 300 samples. ### Disclaimer

While the train/test split is held out, there is a lot of data contamination. The model results can't be trusted for this simple benchmark.
Better tasks for the benchmark will be developed and tested against these models.

License carried over from model, however training data has an undefied license. Check details in Shadertoys.