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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theworker02/open-reason-large"
# 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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

theworker02 commited on 5 days ago

Commit

05c7782

verified ·

1 Parent(s): 33b53d8

Polish large CPU model card (91,544,064 params, host CPU, not 1B)

Browse files

Files changed (1) hide show

README.md +21 -16

README.md CHANGED Viewed

@@ -12,27 +12,32 @@ datasets:
 base_model: gpt2-scratch
 ---
-# Open Reason open-reason-large (CPU)
-This is a **large** GPT-2-style causal LM trained from scratch on the Open Reason SFT split. It is larger than `theworker02/open-reason-medium` and is **not** a 1B model and is **not** `theworker02/open-reason-1b`.
-- Parameters: 91544064
-- Architecture: n_layer=12 n_embd=768 n_head=12
-- Steps: 400
-- Backend: cpu-host
-- CUDA used: False
-- Hardware: Host CPU; torch 2.12.0+cpu; cuda_available=False; docker_installed=False; docker_used=False. NVIDIA CUDA was not used. AMD GPU/ROCm/DirectML were not used.
-- Dataset: theworker02/open-reason pipeline 1.4.0
-- SFT rows: 3175
-- Final loss: 5.73606538772583
-Related checkpoints (none of these is a 1B model):
 - Dataset: https://huggingface.co/datasets/theworker02/open-reason
-- Small: https://huggingface.co/theworker02/open-reason-small
-- Medium: https://huggingface.co/theworker02/open-reason-medium
-- Large: https://huggingface.co/theworker02/open-reason-large
-No Reddit sources. Project license Apache-2.0.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 base_model: gpt2-scratch
 ---
+# Open Reason large (CPU)
+This is a **large** GPT-2-style causal LM trained from scratch on the Open Reason SFT split. It is larger than [`theworker02/open-reason-medium`](https://huggingface.co/theworker02/open-reason-medium) (13,867,008 parameters) and is **not** a 1B model and is **not** `theworker02/open-reason-1b`.
+Weights live on this Hub repo. They are not stored in the GitHub git tree.
+## Training facts
+- **Parameters:** 91,544,064
+- **Architecture:** GPT-2-style from scratch; `n_layer=12`, `n_embd=768`, `n_head=12`, `vocab_size=8192`, `max_seq_len=256`
+- **Steps:** 400 (batch size 2)
+- **Final training loss:** 5.7361 (next-token NLL on training batches; not a benchmark score)
+- **SFT rows:** 3,175 from `data/release/all.jsonl`
+- **Dataset:** [`theworker02/open-reason`](https://huggingface.co/datasets/theworker02/open-reason) pipeline **1.4.0**
+- **License:** Apache-2.0
+- **Hardware:** AMD Ryzen 9 9950X host **CPU**; `torch 2.12.0+cpu`; `torch.cuda.is_available()=False`; Docker **not installed** and **not used**; `nvidia-smi` not present. NVIDIA CUDA was not used. AMD GPU / ROCm / DirectML were not used.
+## Related
 - Dataset: https://huggingface.co/datasets/theworker02/open-reason
+- Small (~1.3M): https://huggingface.co/theworker02/open-reason-small
+- Medium (13,867,008): https://huggingface.co/theworker02/open-reason-medium
+- Large (this repo, 91,544,064): https://huggingface.co/theworker02/open-reason-large
+- GitHub: https://github.com/theworker02/open-reason
+No Reddit sources.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer