Instructions to use Ashraf-kasem/custom_gpt2_frames_text_continue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashraf-kasem/custom_gpt2_frames_text_continue with Transformers:

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

pipe = pipeline("text-generation", model="Ashraf-kasem/custom_gpt2_frames_text_continue")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text_continue")
model = AutoModelForCausalLM.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text_continue", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashraf-kasem/custom_gpt2_frames_text_continue with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ashraf-kasem/custom_gpt2_frames_text_continue"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ashraf-kasem/custom_gpt2_frames_text_continue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text_continue

SGLang

How to use Ashraf-kasem/custom_gpt2_frames_text_continue 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 "Ashraf-kasem/custom_gpt2_frames_text_continue" \
    --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": "Ashraf-kasem/custom_gpt2_frames_text_continue",
		"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 "Ashraf-kasem/custom_gpt2_frames_text_continue" \
        --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": "Ashraf-kasem/custom_gpt2_frames_text_continue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ashraf-kasem/custom_gpt2_frames_text_continue with Docker Model Runner:
```
docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text_continue
```

Ashraf-kasem commited on Jan 28, 2023

Commit

cff68c9

1 Parent(s): d36c85a

Training in progress epoch 88

Browse files

Files changed (6) hide show

README.md +4 -3
checkpoint/extra_data.pickle +1 -1
checkpoint/weights.h5 +1 -1
logs/train/events.out.tfevents.1674736212.ip-10-39-226-229.afrd.expertcity.com.7323.0.v2 +2 -2
logs/validation/events.out.tfevents.1674737933.ip-10-39-226-229.afrd.expertcity.com.7323.1.v2 +2 -2
tf_model.h5 +1 -1

README.md CHANGED Viewed

@@ -14,9 +14,9 @@ probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [Ashraf-kasem/custom_gpt2_frames_text_continue](https://huggingface.co/Ashraf-kasem/custom_gpt2_frames_text_continue) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.6780
-- Validation Loss: 2.2867
-- Epoch: 87
 ## Model description
@@ -130,6 +130,7 @@ The following hyperparameters were used during training:
 | 0.6869     | 2.2838          | 85    |
 | 0.6823     | 2.2853          | 86    |
 | 0.6780     | 2.2867          | 87    |
 ### Framework versions

 This model is a fine-tuned version of [Ashraf-kasem/custom_gpt2_frames_text_continue](https://huggingface.co/Ashraf-kasem/custom_gpt2_frames_text_continue) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Train Loss: 0.6737
+- Validation Loss: 2.2883
+- Epoch: 88
 ## Model description
 | 0.6869     | 2.2838          | 85    |
 | 0.6823     | 2.2853          | 86    |
 | 0.6780     | 2.2867          | 87    |
+| 0.6737     | 2.2883          | 88    |
 ### Framework versions

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