Instructions to use KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4 with Transformers:

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

pipe = pipeline("text-generation", model="KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4")
model = AutoModelForCausalLM.from_pretrained("KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4

SGLang

How to use KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4 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 "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4 with Docker Model Runner:
```
docker model run hf.co/KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4
```

gpt-oss-20b-zeroin-v8-r4

Full fine-tuned variant of openai/gpt-oss-20b on a Korean, domain-specific corpus covering the Zeroin fund-evaluation methodology (fund classification principles, etc.).

This release is distributed under CC BY-NC 4.0 — free for non-commercial research, evaluation, and educational use. See LICENSE and NOTICE.

Model summary

Field	Value
Base model	`openai/gpt-oss-20b` (Apache 2.0)
Architecture	`GptOssForCausalLM` (MoE, 32 experts, 4 active)
Parameters	~21B (full fine-tune)
Precision	bfloat16
Context length	131072 (base) / 4096 (training)
Chat template	Harmony response format (preserved from base)
Training framework	transformers + TRL + DeepSpeed ZeRO-3
Training hardware	3x NVIDIA H200
Training steps	1000
Max length	4096
Per-device batch size	1 (effective batch = 24 with grad accum 8)
Learning rate	1e-5 (cosine, warmup)
Final training loss	~0.0025

Intended use

Korean-language Q&A and reasoning about the Zeroin fund-classification methodology (Sufficiency / Comparability / Continuity principles, etc.).
Research, documentation generation, and agent workflows in non-commercial contexts.

Out-of-scope use

Commercial deployments of any kind (prohibited by CC BY-NC 4.0).
Public-facing deployments without additional safety filtering.
Medical, legal, financial advice to end-users.
Any use not permitted by the base model's gpt-oss usage policy.

Usage (transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "<your Korean prompt here, e.g. a question about Zeroin fund-evaluation methodology>"}
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(
    inputs,
    max_new_tokens=1024,
    do_sample=False,
)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=False))

Training data

The training corpus is published as a companion dataset: KG-ZEROIN/zeroin-v8-r4-dataset (CC BY-NC 4.0). The corpus is derived from the Zeroin fund-evaluation methodology Korean domain document and is organized as a Harmony-ready chat-messages JSONL.

Limitations

The model may reproduce fragments of the training data when prompted with closely matching questions.
Specialized to Korean financial / fund-classification vocabulary; general-domain performance is close to the base model's behavior but has not been independently re-evaluated.
Reasoning traces in the Harmony analysis channel are NOT intended for end-user display.

License and attribution

Weights and accompanying files: CC BY-NC 4.0 (Copyright 2026 KG-ZEROIN).
Base model components: openai/gpt-oss-20b, Apache License 2.0, Copyright 2025 OpenAI. Apache 2.0 attribution is preserved in NOTICE.
Use is additionally governed by OpenAI's gpt-oss usage policy.

See LICENSE (CC BY-NC 4.0) and NOTICE (upstream attribution) in this repository.

Citation (base model)

@misc{openai2025gptoss,
  title={gpt-oss-120b & gpt-oss-20b Model Card},
  author={OpenAI},
  year={2025},
  eprint={2508.10925},
  archivePrefix={arXiv}
}

사사 (Acknowledgment)

사사번호 (Project ID): RQT-25-090024

이 성과는 정부(과학기술정보통신부)의 재원으로 AI컴퓨팅자원활용기반강화(GPU 임차 지원) 사용자 지원사업의 지원을 받아 수행된 연구임.

This research was supported by the AI Computing Infrastructure Enhancement (GPU Rental Support) User Support Program funded by the Ministry of Science and ICT (MSIT), Republic of Korea.

Downloads last month: 9

Safetensors

Model size

21B params

Tensor type

BF16

Model tree for KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4

Base model

openai/gpt-oss-20b

Finetuned

(575)

this model

Paper for KG-ZEROIN/gpt-oss-20b-zeroin-v8-r4

gpt-oss-120b & gpt-oss-20b Model Card

Paper • 2508.10925 • Published Aug 8, 2025 • 32