Instructions to use AdnanRiaz107/CBertbase-APPS10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdnanRiaz107/CBertbase-APPS10k with Transformers:

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

pipe = pipeline("text-generation", model="AdnanRiaz107/CBertbase-APPS10k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AdnanRiaz107/CBertbase-APPS10k")
model = AutoModelForMultimodalLM.from_pretrained("AdnanRiaz107/CBertbase-APPS10k")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AdnanRiaz107/CBertbase-APPS10k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdnanRiaz107/CBertbase-APPS10k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdnanRiaz107/CBertbase-APPS10k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AdnanRiaz107/CBertbase-APPS10k

SGLang

How to use AdnanRiaz107/CBertbase-APPS10k 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 "AdnanRiaz107/CBertbase-APPS10k" \
    --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": "AdnanRiaz107/CBertbase-APPS10k",
		"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 "AdnanRiaz107/CBertbase-APPS10k" \
        --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": "AdnanRiaz107/CBertbase-APPS10k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AdnanRiaz107/CBertbase-APPS10k with Docker Model Runner:
```
docker model run hf.co/AdnanRiaz107/CBertbase-APPS10k
```

CBertbase-APPS10k / model.safetensors

AdnanRiaz107

Training in progress, step 10000

7dd48dc verified about 2 years ago

Download

History Blame

499 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 65a4027e890219ec9a708007203777fef7f131c44985a010390565b8d33e6db3
Size of remote file:: 499 MB
SHA256:: 10a6d02aa9988ed4930710293a6a9ee82bbcc7bb09a6e6a9b20c96daf7091299

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.