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Model: Orionfold/patent-strategist-v3-nemo Source: Original Platform
2026-06-05 18:28:16 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+library_name: transformers
+base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
+pipeline_tag: text-generation
+model_creator: Orionfold LLC
+language:
+  - en
+tags:
+  - transformers
+  - safetensors
+  - bf16
+  - spark-tested
+  - orionfold
+  - "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"
+  - patent
+  - patent-strategist
+  - reasoning
+  - lora-finetune
+  - bakeoff
+  - r1-distill
+  - trained-with-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`. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/manavsehgal/ai-field-notes/blob/main/notebooks/patent-strategist/builder.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/manavsehgal/ai-field-notes/blob/main/notebooks/patent-strategist/builder.ipynb) |
+| **User** | Load the published model and call it from your own app in a few lines. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/manavsehgal/ai-field-notes/blob/main/notebooks/patent-strategist/user.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/manavsehgal/ai-field-notes/blob/main/notebooks/patent-strategist/user.ipynb) |
+
+## 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:
+
+```python
+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](https://ainative.business/field-notes/patent-strategist-bakeoff-unsloth-vs-nemo-framework/).
+
+## 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](#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`](https://huggingface.co/Orionfold/patent-strategist-v3-nemo) | NeMo Framework | BF16 (transformers) |
+| [`Orionfold/patent-strategist-v3-nemo-GGUF`](https://huggingface.co/Orionfold/patent-strategist-v3-nemo-GGUF) | NeMo Framework | GGUF (llama.cpp) |
+
+---
+
+Published by **Orionfold LLC** · [orionfold.com](https://orionfold.com) · Methods documented at [ainative.business/field-notes](https://ainative.business/field-notes/).
+
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,14 @@
+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
+
+' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<｜User｜>' + content + '<｜Assistant｜>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
+' + '```json' + '
+' + tool['function']['arguments'] + '
+' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{content + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
+' + '```json' + '
+' + tool['function']['arguments'] + '
+' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
+' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
+' + '```json' + '
+' + tool['function']['arguments'] + '
+' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + content + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
+<｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 131072,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "attn_factor": 0.8782488562869419,
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768,
+    "rope_theta": 1000000,
+    "rope_type": "yarn"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.3.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
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--- a/tokenizer_config.json
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