Instructions to use theworker02/open-reason-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theworker02/open-reason-xl with Transformers:

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

pipe = pipeline("text-generation", model="theworker02/open-reason-xl")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theworker02/open-reason-xl")
model = AutoModelForCausalLM.from_pretrained("theworker02/open-reason-xl", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theworker02/open-reason-xl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theworker02/open-reason-xl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theworker02/open-reason-xl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/theworker02/open-reason-xl

SGLang

How to use theworker02/open-reason-xl 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 "theworker02/open-reason-xl" \
    --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": "theworker02/open-reason-xl",
		"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 "theworker02/open-reason-xl" \
        --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": "theworker02/open-reason-xl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use theworker02/open-reason-xl with Docker Model Runner:
```
docker model run hf.co/theworker02/open-reason-xl
```

Open Reason XL (CPU)

GPT-2-style causal LM trained from scratch on the Open Reason SFT split. Exact parameter count: 443,719,680. This is not a 1B model and is not theworker02/open-reason-1b.

Training facts

Parameters: 443,719,680 (sum(p.numel() for p in model.parameters()))
Architecture: n_layer=22, n_embd=1280, n_head=20, vocab=8192, seq=256
Steps: 120 (batch 1, gradient accumulation 2, gradient checkpointing)
Device: host CPU (AMD Ryzen 9 9950X, 32 threads)
torch: 2.12.0+cpu; cuda_available=False; Docker not installed
NVIDIA CUDA was not used. AMD GPU / ROCm / DirectML were not used
Dataset: theworker02/open-reason pipeline 1.4.0
SFT rows: 3175 (data/release/all.jsonl)
Final training NLL: 5.8116
License: Apache-2.0
No held-out exact-match / coding / math benchmark scores are claimed

Related checkpoints (none of these is a 1B model):

No Reddit sources. Project license Apache-2.0.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("theworker02/open-reason-xl")
model = AutoModelForCausalLM.from_pretrained("theworker02/open-reason-xl")

Downloads last month: 284

Safetensors

Model size

0.4B params

Tensor type

F32

theworker02
/

open-reason-xl

Open Reason XL (CPU)

Training facts

Dataset used to train theworker02/open-reason-xl