Instructions to use bimabk/instruct_text_c4parity_75742018 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bimabk/instruct_text_c4parity_75742018 with Transformers:

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

pipe = pipeline("text-generation", model="bimabk/instruct_text_c4parity_75742018")

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

tokenizer = AutoTokenizer.from_pretrained("bimabk/instruct_text_c4parity_75742018")
model = AutoModelForCausalLM.from_pretrained("bimabk/instruct_text_c4parity_75742018", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bimabk/instruct_text_c4parity_75742018 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bimabk/instruct_text_c4parity_75742018"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bimabk/instruct_text_c4parity_75742018",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bimabk/instruct_text_c4parity_75742018

SGLang

How to use bimabk/instruct_text_c4parity_75742018 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 "bimabk/instruct_text_c4parity_75742018" \
    --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": "bimabk/instruct_text_c4parity_75742018",
		"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 "bimabk/instruct_text_c4parity_75742018" \
        --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": "bimabk/instruct_text_c4parity_75742018",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use bimabk/instruct_text_c4parity_75742018 with Docker Model Runner:
```
docker model run hf.co/bimabk/instruct_text_c4parity_75742018
```

instruct_text_c4parity_75742018 / tokenizer.json

bimabk

Upload task output 2d5fcd5c-5061-43f0-aee0-85fb7da5f417

a01b98c verified 3 months ago

History Blame Contribute Delete

11.4 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:: 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
Size of remote file:: 11.4 MB
SHA256:: 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa

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