Instructions to use Orionfold/patent-strategist-v3-nemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Orionfold/patent-strategist-v3-nemo with Transformers:

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

pipe = pipeline("text-generation", model="Orionfold/patent-strategist-v3-nemo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Orionfold/patent-strategist-v3-nemo")
model = AutoModelForCausalLM.from_pretrained("Orionfold/patent-strategist-v3-nemo")
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 Orionfold/patent-strategist-v3-nemo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Orionfold/patent-strategist-v3-nemo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Orionfold/patent-strategist-v3-nemo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Orionfold/patent-strategist-v3-nemo

SGLang

How to use Orionfold/patent-strategist-v3-nemo 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 "Orionfold/patent-strategist-v3-nemo" \
    --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": "Orionfold/patent-strategist-v3-nemo",
		"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 "Orionfold/patent-strategist-v3-nemo" \
        --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": "Orionfold/patent-strategist-v3-nemo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Orionfold/patent-strategist-v3-nemo with Docker Model Runner:
```
docker model run hf.co/Orionfold/patent-strategist-v3-nemo
```

patent-strategist v3 — NeMo Framework lane (BF16 HF)

safetensors BF16 merged weights of a LoRA fine-tune of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B on a 5,000-row synthetic patent-reasoning corpus, trained with NeMo Framework on a NVIDIA DGX Spark (GB10, 128 GB unified memory).

What this model does

Offline patent-prosecution reasoning on Spark-class hardware

Patent prosecution work — claim construction, MPEP-grounded office-action responses, Markush analysis, doctrine-of-equivalents reasoning — happens inside firms that can't ship privileged client text to a hosted frontier API. This release distills DeepSeek-R1's chain-of-thought reasoning onto a 5,000-row synthetic patent-reasoning corpus so a single Spark-class box can run the workflow offline, with full IRAC-shaped reasoning chains.

Use cases:

Claim construction (Markush groups, doctrine of equivalents)
MPEP-grounded office-action argument drafting
Prior-art relevance + non-obviousness reasoning chains
Patent-licensing scenario analysis (most-favored-licensee, FTO)

Who this is for: Patent attorneys, prosecution-team engineers, and IP-strategy teams running privileged workflows offline on Spark-class hardware (GB10, 128 GB unified memory) or comparable edge devices.

Notebooks

Two runnable notebooks ship with this model — open either on a free cloud GPU:

Notebook	What it does	Open
Builder	Reproduce this model's build and DGX Spark benchmarks end-to-end with `fieldkit`.
User	Load the published model and call it from your own app in a few lines.

Choosing this lane

NeMo Framework-trained BF16 merged weights — the bakeoff-winning lane. Pick this for production-grade inference via Triton / TensorRT-LLM, for continued fine-tuning in NeMo's PEFT recipe stack, or to export to other quantization paths. The bakeoff measured 5h 38m training wall on this lane (-26% vs the Unsloth baseline on the same recipe) at probe think rate 0.80 / mean chain 1,320 tokens (+44% reasoning depth over the Unsloth baseline). For pure inference on Spark-class hardware, the GGUF sibling is faster.

Spark measurements (BF16 merged):

Variant	Size	Train wall	Probe think rate	Mean chain
BF16	15.26 GB	5h 38m	0.80	1,320 tok

How to run

HuggingFace Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Orionfold/patent-strategist-v3-nemo"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

prompt = (
    "<｜User｜>A patent claim recites \"a fastener selected from the group consisting "
    "of bolts, screws, and rivets.\" Walk through the Markush-group construction "
    "and explain how doctrine of equivalents applies to a magnetic snap.<｜Assistant｜>"
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, temperature=0.6, top_p=0.95)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Methods

Full methodology and Spark-side measurement protocol: Two paths to the same chain — Unsloth vs NeMo Framework on Spark.

Known drift

Bounded limitations observed during Spark-side measurement. Each item below names the artifact and the scope of the drift; the balance of the bench measures clean — see Methods for the full breakdown.

"metes-and-times" terminology — Two known terminology drifts inherited from the v3 synthetic corpus; balance of probe answers (~99%) cite real MPEP sections. Correct legal term in claim construction is metes and bounds.
Fabricated MPEP §2163.05(s) citation — Same scope — corpus-generator artifact, not a model-wide hallucination pattern. Real §2163.05 has subsections (a)–(f) on written-description support; subsection (s) does not exist.

Other Orionfold variants

Sibling repos from the same release:

Variant	Lane	Format
`Orionfold/patent-strategist-v3-nemo`	NeMo Framework	BF16 (transformers)
`Orionfold/patent-strategist-v3-nemo-GGUF`	NeMo Framework	GGUF (llama.cpp)

Published by Orionfold LLC · orionfold.com · Methods documented at ainative.business/field-notes.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Orionfold/patent-strategist-v3-nemo

Base model

deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Finetuned

(54)

this model