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)

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

tokenizer = AutoTokenizer.from_pretrained("pat-jj/harness-1")
model = AutoModelForCausalLM.from_pretrained("pat-jj/harness-1", 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 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
```

harness-1 / README.md

pat-jj

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	---
	library_name: transformers
	base_model: openai/gpt-oss-20b
	tags:
	- text-generation
	- search
	- retrieval
	- harness-1
	---

	# 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](https://huggingface.co/datasets/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.

	![Harness-1 average search performance](https://raw.githubusercontent.com/pat-jj/harness-1/main/assets/teaser_recall_barchart.png)

	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](https://huggingface.co/datasets/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.

	```python
	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",
	)
	```