Instructions to use GabSo/santacoder-finetuned-robot4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GabSo/santacoder-finetuned-robot4 with Transformers:

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

pipe = pipeline("text-generation", model="GabSo/santacoder-finetuned-robot4", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GabSo/santacoder-finetuned-robot4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GabSo/santacoder-finetuned-robot4", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GabSo/santacoder-finetuned-robot4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GabSo/santacoder-finetuned-robot4"
# 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-robot4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GabSo/santacoder-finetuned-robot4

SGLang

How to use GabSo/santacoder-finetuned-robot4 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-robot4" \
    --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-robot4",
		"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-robot4" \
        --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-robot4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GabSo/santacoder-finetuned-robot4 with Docker Model Runner:
```
docker model run hf.co/GabSo/santacoder-finetuned-robot4
```

santacoder-finetuned-robot4

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

Loss: 0.5116

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.05	1	1.5720
No log	0.1	2	2.6819
No log	0.15	3	1.3777
No log	0.2	4	1.7661
No log	0.25	5	1.0597
No log	0.3	6	1.1801
No log	0.35	7	0.8989
No log	0.4	8	0.9572
No log	0.45	9	0.7408
1.48	0.5	10	0.7517
1.48	0.55	11	0.6493
1.48	0.6	12	0.6206
1.48	0.65	13	0.5872
1.48	0.7	14	0.5644
1.48	0.75	15	0.5415
1.48	0.8	16	0.5298
1.48	0.85	17	0.5186
1.48	0.9	18	0.5128
1.48	0.95	19	0.5120
0.5307	1.0	20	0.5116

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

F32

Model tree for GabSo/santacoder-finetuned-robot4

Base model

bigcode/santacoder

Finetuned

(15)

this model