Instructions to use pat-jj/harness-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pat-jj/harness-1 with Transformers:

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

pipe = pipeline("text-generation", model="pat-jj/harness-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("pat-jj/harness-1")
model = AutoModelForMultimodalLM.from_pretrained("pat-jj/harness-1")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pat-jj/harness-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pat-jj/harness-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pat-jj/harness-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pat-jj/harness-1

SGLang

How to use pat-jj/harness-1 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 "pat-jj/harness-1" \
    --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": "pat-jj/harness-1",
		"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 "pat-jj/harness-1" \
        --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": "pat-jj/harness-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pat-jj/harness-1 with Docker Model Runner:
```
docker model run hf.co/pat-jj/harness-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Harness-1

A 20B search agent that matches frontier AI's search capability.

News

2026-06-15: Released Harness-1 training data and retrieval corpora at pat-jj/harness-1-train-data: 899 SFT trajectories, 3,453 RL SEC train-split examples, and corpus shards for BrowseComp+, web, patents, and SEC.
2026-06-15: Released the full merged Harness-1 checkpoint as standard Hugging Face safetensors shards.

Code: https://github.com/pat-jj/harness-1

Paper: https://arxiv.org/abs/2606.02373

Training data and corpora: https://huggingface.co/datasets/pat-jj/harness-1-train-data

Tinker inference example: https://github.com/pat-jj/harness-1/blob/main/inference/tinker_inference.md

vLLM inference example: https://github.com/pat-jj/harness-1/blob/main/inference/vllm_h100_browsecompplus.md

This repository contains the full merged Harness-1 release checkpoint. The model is merged into the openai/gpt-oss-20b base model and saved as standard Hugging Face safetensors shards.

Training Data And Corpora

The Harness-1 training data and retrieval corpora are published separately as pat-jj/harness-1-train-data. It contains one train split with a stage column:

sft: 899 raw GPT-5.4-generated v8d SFT trajectories produced by generate_sft_ultra_0417.py.
rl: 3,453 SEC training-split query records used for RL (TRAIN_DATASETS=sec, RL_QUERY_SPLIT=train).

The same dataset repo also includes retrieval corpora under corpora/:

corpora/browsecompplus/test: 1,144,886 chunks.
corpora/web/train: 219,388 chunks.
corpora/web/test: 54,735 chunks.
corpora/patents/train: 104,842 chunks.
corpora/patents/test: 35,551 chunks.
corpora/sec/train: 2,115,106 chunks.

from datasets import load_dataset

ds = load_dataset("pat-jj/harness-1-train-data", split="train")
sft = ds.filter(lambda row: row["stage"] == "sft")
rl = ds.filter(lambda row: row["stage"] == "rl")

sec_corpus = load_dataset(
    "parquet",
    data_files="hf://datasets/pat-jj/harness-1-train-data/corpora/sec/train/*.parquet",
    split="train",
)

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Safetensors

Model size

21B params

Tensor type

BF16

Model tree for pat-jj/harness-1

Base model

openai/gpt-oss-20b

Finetuned

(535)

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Quantizations

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Paper for pat-jj/harness-1

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses

Paper • 2606.02373 • Published 16 days ago • 54