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

Libraries

How to use MrezaPRZ/codegemma_create_context with Transformers:

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

pipe = pipeline("text-generation", model="MrezaPRZ/codegemma_create_context")

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

tokenizer = AutoTokenizer.from_pretrained("MrezaPRZ/codegemma_create_context")
model = AutoModelForCausalLM.from_pretrained("MrezaPRZ/codegemma_create_context", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MrezaPRZ/codegemma_create_context with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MrezaPRZ/codegemma_create_context

SGLang

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

Docker Model Runner
How to use MrezaPRZ/codegemma_create_context with Docker Model Runner:
```
docker model run hf.co/MrezaPRZ/codegemma_create_context
```

codegemma_create_context / tokenizer.json

MrezaPRZ

Upload tokenizer

011691c verified over 2 years ago

History Blame

17.5 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:: 682eaf7164e8ccca9cdbf8b7427aad86823b64947bd9de31d5f25f26f2bb69a1
Size of remote file:: 17.5 MB
SHA256:: 2879e530a4cd7c2a4581198c4dfec0a3e4fb44531831bd7a5eef785e1d170f58

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