Instructions to use GabSo/santacoder-finetuned-robot2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GabSo/santacoder-finetuned-robot2 with Transformers:

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

pipe = pipeline("text-generation", model="GabSo/santacoder-finetuned-robot2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GabSo/santacoder-finetuned-robot2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GabSo/santacoder-finetuned-robot2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GabSo/santacoder-finetuned-robot2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GabSo/santacoder-finetuned-robot2"
# 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-robot2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GabSo/santacoder-finetuned-robot2

SGLang

How to use GabSo/santacoder-finetuned-robot2 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-robot2" \
    --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-robot2",
		"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-robot2" \
        --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-robot2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GabSo/santacoder-finetuned-robot2 with Docker Model Runner:
```
docker model run hf.co/GabSo/santacoder-finetuned-robot2
```

santacoder-finetuned-robot2

This model is a fine-tuned version of bigcode/santacoder on the dataset datas.csv (généré par gpt3.5-turbo à partir de quelqes exemples). It achieves the following results on the evaluation set:

Loss: 0.6283

Model description

More information needed

Intended uses & limitations

Ce modèle permet de commander un robot à partir d'instruction en langage naturel.

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	1.5944
No log	0.1	2	2.2587
No log	0.15	3	1.3593
No log	0.2	4	1.6304
No log	0.25	5	1.3971
No log	0.3	6	1.2113
No log	0.35	7	0.8876
No log	0.4	8	0.9664
No log	0.45	9	0.8842
1.4437	0.5	10	0.7931
1.4437	0.55	11	0.7410
1.4437	0.6	12	0.7020
1.4437	0.65	13	0.6665
1.4437	0.7	14	0.6705
1.4437	0.75	15	0.6589
1.4437	0.8	16	0.6395
1.4437	0.85	17	0.6358
1.4437	0.9	18	0.6324
1.4437	0.95	19	0.6286
0.5726	1.0	20	0.6283

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

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