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")

# 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")

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

3f3dcd2

1 Parent(s): 30e0d08

last training

Browse files

Files changed (2) hide show

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

logs/train/events.out.tfevents.1674736212.ip-10-39-226-229.afrd.expertcity.com.7323.0.v2 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:19426900fc39e4e566d67d24cb359eac698c3f0a8eb109c2bda2255a50456a75
-size 13611331

 version https://git-lfs.github.com/spec/v1
+oid sha256:dfab9f6945b48879b9b0d1bbfb5e8b9893a36bab1ab34b40a963b58cc4dba775
+size 13736203

logs/validation/events.out.tfevents.1674737933.ip-10-39-226-229.afrd.expertcity.com.7323.1.v2 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:94a6c2fa5d974c21fb4ab79f7f01255ab7f6daa2a38172e544e40597d899f118
-size 15668

 version https://git-lfs.github.com/spec/v1
+oid sha256:ef9d4c45f50549273769dfe00931f91e483a0539fff8d88672b36c8ef4fa680a
+size 15736