Instructions to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Transformers:

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

pipe = pipeline("text-generation", model="razor5050/codex-qwen2-5-0-5b-unsloth-codex1m")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("razor5050/codex-qwen2-5-0-5b-unsloth-codex1m", device_map="auto")

PEFT
How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m

SGLang

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m 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 "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
    --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": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"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 "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
        --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": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Docker Model Runner:
```
docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m
```

codex-qwen2-5-0-5b-unsloth-codex1m / checkpoints_under8000 /checkpoint-7001 /adapter_model.safetensors

razor5050

Upload under8000 checkpoint step 7001

ddb6d72 verified 4 months ago

Download

History Blame

70.4 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:: b46ed5bd9437f6e86c5c9a087e7e8b52c3cea5ec7c140fd986491030c8e0a972
Size of remote file:: 70.4 MB
SHA256:: 67f2a31f5e7a367ed9a02bde204c8d7637d3cc53335519fdc2cb4642a77d1fcc

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