Instructions to use xemha125/Rx_Codex_V1_Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xemha125/Rx_Codex_V1_Tiny with Transformers:

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

pipe = pipeline("text-generation", model="xemha125/Rx_Codex_V1_Tiny")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xemha125/Rx_Codex_V1_Tiny")
model = AutoModelForCausalLM.from_pretrained("xemha125/Rx_Codex_V1_Tiny")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xemha125/Rx_Codex_V1_Tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xemha125/Rx_Codex_V1_Tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xemha125/Rx_Codex_V1_Tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xemha125/Rx_Codex_V1_Tiny

SGLang

How to use xemha125/Rx_Codex_V1_Tiny 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 "xemha125/Rx_Codex_V1_Tiny" \
    --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": "xemha125/Rx_Codex_V1_Tiny",
		"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 "xemha125/Rx_Codex_V1_Tiny" \
        --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": "xemha125/Rx_Codex_V1_Tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xemha125/Rx_Codex_V1_Tiny with Docker Model Runner:
```
docker model run hf.co/xemha125/Rx_Codex_V1_Tiny
```

Rx_Codex_V1_Tiny

Commit History

Update README.md

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Rx MHA commited on Sep 3, 2025

Update README.md

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Rx MHA commited on Aug 15, 2025

Upload tokenizer

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Rx MHA commited on Aug 15, 2025

Upload model

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Rx MHA commited on Aug 15, 2025

initial commit

8f8e80f
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Rx MHA commited on Aug 15, 2025

Commit History

Update README.md a2a4694 verified

Update README.md 113b409 verified

Upload tokenizer bcc7fca verified

Upload model ce5df29 verified

initial commit 8f8e80f verified

Update README.md

a2a4694
verified

Update README.md

113b409
verified

Upload tokenizer

bcc7fca
verified

Upload model

ce5df29
verified

initial commit

8f8e80f
verified