Instructions to use juancopi81/bach_sweeps_best_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juancopi81/bach_sweeps_best_model with Transformers:

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

pipe = pipeline("text-generation", model="juancopi81/bach_sweeps_best_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("juancopi81/bach_sweeps_best_model")
model = AutoModelForCausalLM.from_pretrained("juancopi81/bach_sweeps_best_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use juancopi81/bach_sweeps_best_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "juancopi81/bach_sweeps_best_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "juancopi81/bach_sweeps_best_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/juancopi81/bach_sweeps_best_model

SGLang

How to use juancopi81/bach_sweeps_best_model 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 "juancopi81/bach_sweeps_best_model" \
    --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": "juancopi81/bach_sweeps_best_model",
		"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 "juancopi81/bach_sweeps_best_model" \
        --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": "juancopi81/bach_sweeps_best_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use juancopi81/bach_sweeps_best_model with Docker Model Runner:
```
docker model run hf.co/juancopi81/bach_sweeps_best_model
```

output

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5751
Accuracy: 0.0021

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: 0.0006058454513356471
train_batch_size: 16
eval_batch_size: 32
seed: 1
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2205	1.25	315	0.8209	0.0010
0.813	2.51	630	0.7684	0.0009
0.7645	3.76	945	0.7393	0.0008
0.7249	5.02	1260	0.6980	0.0007
0.6832	6.27	1575	0.6646	0.0003
0.6426	7.53	1890	0.6371	0.0019
0.6034	8.78	2205	0.6041	0.0020
0.564	10.04	2520	0.5897	0.0018
0.5253	11.29	2835	0.5857	0.0018
0.4961	12.55	3150	0.5771	0.0017
0.4752	13.8	3465	0.5751	0.0021

Framework versions

Transformers 4.29.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Downloads last month: 6

Safetensors

Model size

14M params

Tensor type

F32

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