Instructions to use NasimB/gpt2-concat-second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NasimB/gpt2-concat-second with Transformers:

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

pipe = pipeline("text-generation", model="NasimB/gpt2-concat-second")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NasimB/gpt2-concat-second")
model = AutoModelForCausalLM.from_pretrained("NasimB/gpt2-concat-second")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NasimB/gpt2-concat-second with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NasimB/gpt2-concat-second"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NasimB/gpt2-concat-second",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NasimB/gpt2-concat-second

SGLang

How to use NasimB/gpt2-concat-second 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 "NasimB/gpt2-concat-second" \
    --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": "NasimB/gpt2-concat-second",
		"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 "NasimB/gpt2-concat-second" \
        --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": "NasimB/gpt2-concat-second",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NasimB/gpt2-concat-second with Docker Model Runner:
```
docker model run hf.co/NasimB/gpt2-concat-second
```

NasimB commited on Jul 4, 2023

Commit

fed2e05

1 Parent(s): d4eedf4

update model card README.md

Browse files

Files changed (1) hide show

README.md +35 -35

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
-- Loss: 4.4050
 ## Model description
@@ -49,40 +49,40 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
-| 6.7153        | 0.29  | 500   | 5.6161          |
-| 5.3436        | 0.58  | 1000  | 5.1899          |
-| 4.9993        | 0.87  | 1500  | 4.9392          |
-| 4.7264        | 1.16  | 2000  | 4.7817          |
-| 4.5632        | 1.45  | 2500  | 4.6599          |
-| 4.4515        | 1.74  | 3000  | 4.5490          |
-| 4.3483        | 2.02  | 3500  | 4.4674          |
-| 4.1412        | 2.31  | 4000  | 4.4283          |
-| 4.1268        | 2.6   | 4500  | 4.3805          |
-| 4.0932        | 2.89  | 5000  | 4.3336          |
-| 3.9281        | 3.18  | 5500  | 4.3330          |
-| 3.8693        | 3.47  | 6000  | 4.3021          |
-| 3.8701        | 3.76  | 6500  | 4.2746          |
-| 3.8108        | 4.05  | 7000  | 4.2753          |
-| 3.6096        | 4.34  | 7500  | 4.2838          |
-| 3.6425        | 4.63  | 8000  | 4.2588          |
-| 3.6484        | 4.92  | 8500  | 4.2325          |
-| 3.4344        | 5.21  | 9000  | 4.2856          |
-| 3.3896        | 5.49  | 9500  | 4.2764          |
-| 3.4182        | 5.78  | 10000 | 4.2599          |
-| 3.3427        | 6.07  | 10500 | 4.2920          |
-| 3.1434        | 6.36  | 11000 | 4.3128          |
-| 3.164         | 6.65  | 11500 | 4.3048          |
-| 3.1778        | 6.94  | 12000 | 4.2961          |
-| 2.9609        | 7.23  | 12500 | 4.3472          |
-| 2.9349        | 7.52  | 13000 | 4.3537          |
-| 2.9521        | 7.81  | 13500 | 4.3518          |
-| 2.8837        | 8.1   | 14000 | 4.3753          |
-| 2.7663        | 8.39  | 14500 | 4.3885          |
-| 2.771         | 8.68  | 15000 | 4.3923          |
-| 2.7798        | 8.96  | 15500 | 4.3920          |
-| 2.6934        | 9.25  | 16000 | 4.4025          |
-| 2.685         | 9.54  | 16500 | 4.4043          |
-| 2.688         | 9.83  | 17000 | 4.4050          |
 ### Framework versions

 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
+- Loss: 4.4031
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
+| 6.7063        | 0.29  | 500   | 5.6161          |
+| 5.3409        | 0.58  | 1000  | 5.1879          |
+| 4.9975        | 0.87  | 1500  | 4.9292          |
+| 4.7248        | 1.16  | 2000  | 4.7819          |
+| 4.5625        | 1.45  | 2500  | 4.6577          |
+| 4.4518        | 1.74  | 3000  | 4.5536          |
+| 4.3506        | 2.02  | 3500  | 4.4718          |
+| 4.1444        | 2.31  | 4000  | 4.4324          |
+| 4.1299        | 2.6   | 4500  | 4.3859          |
+| 4.097         | 2.89  | 5000  | 4.3383          |
+| 3.9322        | 3.18  | 5500  | 4.3372          |
+| 3.8738        | 3.47  | 6000  | 4.3092          |
+| 3.8743        | 3.76  | 6500  | 4.2795          |
+| 3.8147        | 4.05  | 7000  | 4.2758          |
+| 3.6152        | 4.34  | 7500  | 4.2857          |
+| 3.6479        | 4.63  | 8000  | 4.2632          |
+| 3.654         | 4.92  | 8500  | 4.2380          |
+| 3.4411        | 5.21  | 9000  | 4.2846          |
+| 3.398         | 5.49  | 9500  | 4.2785          |
+| 3.4249        | 5.78  | 10000 | 4.2628          |
+| 3.3498        | 6.07  | 10500 | 4.2910          |
+| 3.1525        | 6.36  | 11000 | 4.3119          |
+| 3.1727        | 6.65  | 11500 | 4.3057          |
+| 3.1862        | 6.94  | 12000 | 4.2985          |
+| 2.9723        | 7.23  | 12500 | 4.3475          |
+| 2.9448        | 7.52  | 13000 | 4.3551          |
+| 2.9617        | 7.81  | 13500 | 4.3526          |
+| 2.8946        | 8.1   | 14000 | 4.3748          |
+| 2.7783        | 8.39  | 14500 | 4.3866          |
+| 2.7819        | 8.68  | 15000 | 4.3904          |
+| 2.7913        | 8.96  | 15500 | 4.3905          |
+| 2.7052        | 9.25  | 16000 | 4.4009          |
+| 2.6969        | 9.54  | 16500 | 4.4029          |
+| 2.7           | 9.83  | 17000 | 4.4031          |
 ### Framework versions