Instructions to use Lakoc/TED_CLM_gpt2_tedlium_bigger_lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lakoc/TED_CLM_gpt2_tedlium_bigger_lr with Transformers:

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

pipe = pipeline("text-generation", model="Lakoc/TED_CLM_gpt2_tedlium_bigger_lr")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lakoc/TED_CLM_gpt2_tedlium_bigger_lr")
model = AutoModelForCausalLM.from_pretrained("Lakoc/TED_CLM_gpt2_tedlium_bigger_lr", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lakoc/TED_CLM_gpt2_tedlium_bigger_lr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lakoc/TED_CLM_gpt2_tedlium_bigger_lr

SGLang

How to use Lakoc/TED_CLM_gpt2_tedlium_bigger_lr 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 "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr" \
    --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": "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr",
		"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 "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr" \
        --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": "Lakoc/TED_CLM_gpt2_tedlium_bigger_lr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lakoc/TED_CLM_gpt2_tedlium_bigger_lr with Docker Model Runner:
```
docker model run hf.co/Lakoc/TED_CLM_gpt2_tedlium_bigger_lr
```

TED_CLM_gpt2_tedlium_bigger_lr

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.8755
Accuracy: 0.5540

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: 0.004
train_batch_size: 128
eval_batch_size: 128
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 512
total_eval_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20000
num_epochs: 15.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0351	0.62	3000	2.2280	0.4798
1.9186	1.24	6000	2.0994	0.5074
1.88	1.86	9000	2.0577	0.5142
1.8505	2.49	12000	2.0113	0.5223
1.8284	3.11	15000	1.9957	0.5279
1.8182	3.73	18000	1.9891	0.5305
1.8061	4.35	21000	1.9617	0.5371
1.7969	4.97	24000	1.9413	0.5369
2.0383	5.59	27000	2.1697	0.4894
1.7668	6.22	30000	1.9366	0.5397
1.7556	6.84	33000	1.9303	0.5402
1.7492	7.46	36000	1.9140	0.5432
1.7409	8.08	39000	1.9088	0.5445
1.7317	8.7	42000	1.9030	0.5455
1.7218	9.32	45000	1.9040	0.5496
1.7261	9.94	48000	1.8952	0.5506
1.7175	10.57	51000	1.8959	0.5498
1.708	11.19	54000	1.8909	0.5510
1.7056	11.81	57000	1.8917	0.5518
1.6971	12.43	60000	1.8879	0.5523
1.6986	13.05	63000	1.8790	0.5532
1.6972	13.67	66000	1.8799	0.5526
1.6858	14.29	69000	1.8782	0.5543
1.6875	14.92	72000	1.8755	0.5540

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.13.1
Tokenizers 0.13.3

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