Instructions to use GabSo/santacoder-finetuned-the-stack-bash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GabSo/santacoder-finetuned-the-stack-bash with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GabSo/santacoder-finetuned-the-stack-bash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GabSo/santacoder-finetuned-the-stack-bash", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GabSo/santacoder-finetuned-the-stack-bash", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GabSo/santacoder-finetuned-the-stack-bash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GabSo/santacoder-finetuned-the-stack-bash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GabSo/santacoder-finetuned-the-stack-bash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GabSo/santacoder-finetuned-the-stack-bash

SGLang

How to use GabSo/santacoder-finetuned-the-stack-bash with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GabSo/santacoder-finetuned-the-stack-bash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GabSo/santacoder-finetuned-the-stack-bash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GabSo/santacoder-finetuned-the-stack-bash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GabSo/santacoder-finetuned-the-stack-bash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GabSo/santacoder-finetuned-the-stack-bash with Docker Model Runner:
```
docker model run hf.co/GabSo/santacoder-finetuned-the-stack-bash
```

santacoder-finetuned-the-stack-bash

This model is a fine-tuned version of bigcode/santacoder on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8294

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1
training_steps: 10

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.1	1	1.6955
No log	0.2	2	3.6096
No log	0.3	3	1.5787
No log	0.4	4	1.8131
No log	0.5	5	1.0994
No log	0.6	6	1.0921
No log	0.7	7	0.9509
No log	0.8	8	0.8762
No log	0.9	9	0.8375
1.3831	1.0	10	0.8294

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

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