Instructions to use xemha125/RxCodexV1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xemha125/RxCodexV1-mini with Transformers:

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

pipe = pipeline("text-generation", model="xemha125/RxCodexV1-mini")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xemha125/RxCodexV1-mini")
model = AutoModelForCausalLM.from_pretrained("xemha125/RxCodexV1-mini")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xemha125/RxCodexV1-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xemha125/RxCodexV1-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xemha125/RxCodexV1-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xemha125/RxCodexV1-mini

SGLang

How to use xemha125/RxCodexV1-mini 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/RxCodexV1-mini" \
    --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/RxCodexV1-mini",
		"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/RxCodexV1-mini" \
        --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/RxCodexV1-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xemha125/RxCodexV1-mini with Docker Model Runner:
```
docker model run hf.co/xemha125/RxCodexV1-mini
```

RxCodexV1-mini

Commit History

Update tokenizer alongside model at 173000 steps

b8c5763
verified

Rx MHA commited on May 6, 2025

Update model to 173000 steps

ae0dada
verified

Rx MHA commited on May 6, 2025

Upload tokenizer for RxCodexV1-mini

d9a8e3f
verified

Rx MHA commited on May 3, 2025

Upload RxCodexV1-mini base model (1 epoch)

9db3a19
verified

Rx MHA commited on May 3, 2025

initial commit

bed955b
verified

Rx MHA commited on May 3, 2025

Commit History

Update tokenizer alongside model at 173000 steps b8c5763 verified

Update model to 173000 steps ae0dada verified

Upload tokenizer for RxCodexV1-mini d9a8e3f verified

Upload RxCodexV1-mini base model (1 epoch) 9db3a19 verified

initial commit bed955b verified

Update tokenizer alongside model at 173000 steps

b8c5763
verified

Update model to 173000 steps

ae0dada
verified

Upload tokenizer for RxCodexV1-mini

d9a8e3f
verified

Upload RxCodexV1-mini base model (1 epoch)

9db3a19
verified

initial commit

bed955b
verified