diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..ffe63ffd1ed910847e579aa9e9306a19dbea2f78 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +dory-decisions-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +nvfp4/tokenizer.json filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..ec297ac5456384786644013ec196da33b916be97 --- /dev/null +++ b/LICENSE @@ -0,0 +1,49 @@ +OpenMDW License Agreement, version 1.1 (OpenMDW-1.1) + +By exercising rights granted to you under this agreement, you accept and agree +to its terms. + +As used in this agreement, "Model Materials" means the materials provided to +you under this agreement, consisting of: (1) one or more machine learning +models (including architecture and parameters); and (2) all related artifacts +(including associated data, documentation and software) that are provided to +you hereunder. + +Subject to your compliance with this agreement, permission is hereby granted, +free of charge, to deal in the Model Materials without restriction, including +under all copyright, patent, database, and trade secret rights included or +embodied therein. + +If you distribute any portion of the Model Materials, you shall retain in your +distribution (1) a copy of this agreement, and (2) all copyright notices and +other notices of origin included in the Model Materials that are applicable to +your distribution. + +If you file, maintain, or voluntarily participate in a lawsuit against any +person or entity asserting that the Model Materials directly or indirectly +infringe any patent or copyright, then all rights and grants made to you +hereunder are terminated, unless that lawsuit was in response to a +corresponding lawsuit first brought against you. + +This agreement does not impose any restrictions or obligations with respect to +any use, modification, or sharing of any outputs generated by using the Model +Materials. + +THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE +ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL +TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW. + +YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY +APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION +ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL +MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS +REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE +DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR +ANYTHING INCORPORATED OR EMBODIED THEREIN. + +IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE +USE THEREOF OR OTHER DEALINGS THEREIN. diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..7ffa6f1ec9f637aea3220b913917adfb89780b66 --- /dev/null +++ b/NOTICE @@ -0,0 +1,7 @@ +Derived from nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16, revision b366b87f762ea334ba3eb55912031baba1515810. +Original model: NVIDIA. Base model licence: OpenMDW-1.1. +Modifications: typed-decision LoRA fine-tuning, action selection, state tracking, planning, human-judgement training, BF16 merging, and NVFP4 quantization. +Training source attribution, pinned versions, and recorded source licences appear in README.md and training-data.json. +SCONE attribution: Long, Pasupat and Liang (2016). HelpSteer2 attribution: NVIDIA and Scale AI. + +Additional export: Q8_0 GGUF with Dory chat and typed-decision prompt metadata for llama.cpp. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3b0fd6548e6323b516a4c0b27024d903c70827f4 --- /dev/null +++ b/README.md @@ -0,0 +1,391 @@ +--- +license: other +license_name: openmdw-1.1 +license_link: LICENSE +base_model: nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16 +base_model_relation: finetune +pipeline_tag: text-generation +language: +- en +tags: +- dory +- decision-making +- lora +- eider +- bf16 +- nvfp4 +- gguf +- llama-cpp +datasets: +- ZefanCai/Open-Jev +- SargeDev/jev-distill-corpus-v3 +- tasksource/procedural-typed-decisions +- ZefanCai/Open-Jev-v1.1 +- Team-ACE/ToolACE +- ehovy/race +- openai/gsm8k +- google/code_x_glue_cc_defect_detection +- jat-project/jat-dataset +- osunlp/early-experience +- yyyyyyjjjjzzz/competitive-game-dataset +- nvidia/HelpSteer2 +--- + +# Dory 1.0 4B Typed Decisions + +**Release: `state-32k`.** A Dory fine-tune for fast typed decisions, action selection, and state tracking in software agents. +This release continues `actions-48k` with 32,000 questions about state tracking, planning, human judgements, and earlier tasks. +On 512 reserved new questions, native NVFP4 accuracy improves from **46.48% to 75.59%**. +The main gains come from state tracking and planning. Human-judgement accuracy remains almost unchanged. + +The base model is [NVIDIA Dory 1.0 4B](https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16). +An application supplies a state, a question, and permitted answers. +The model scores those answers and returns a binary probability, a named choice, or an ordered score. + +This experiment belongs to [Eider](https://github.com/rdaum/eider), a Rust/CUDA inference server for NVIDIA DGX Spark. +Eider provides the evaluated prompt format and an optimized serving implementation. +The learned behaviour can also run in another engine that implements Dory and the same decision format. + +## Available formats + +All three formats contain the same fine-tuned `state-32k` model. They share this model card and training history. + +| Format | Location | Use | +|---|---|---| +| BF16 safetensors | Root: eight shards and `model.safetensors.index.json` | Merged weights for a runtime with Dory support. | +| NVFP4 safetensors | [`nvfp4/`](nvfp4) | Eider's evaluated packed 4-bit checkpoint, with its config and tokenizer. | +| Q8_0 GGUF | [`dory-decisions-Q8_0.gguf`](dory-decisions-Q8_0.gguf), 4.23 GB | llama.cpp generation and native `/v1/systemone` decisions; instructions below. | +| Rank-8 LoRA adapter | [`adapter/`](adapter/README.md) | Standalone training changes for the original NVIDIA BF16 model. | + +BF16 is the merged full-precision release; NVFP4 and Q8_0 are its quantized exports. +The existing [BF16 repository](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-bf16) +and [NVFP4 repository](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-nvfp4) remain available. + +The previous weights and calibration remain available under the [`actions-48k`](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-bf16/tree/actions-48k) tag. +This release has **no fitted probability-calibration profile**. + +## What this is for + +The intended use is a small, local decision component inside a larger application or agent. +Examples include tool-call validation, workflow routing, document-grounding judgements, and assessments against a rubric. +The application defines the question and answer meanings at request time, so the model can serve many classification tasks. + +The objective is a fast “System 1” judgement without an autoregressive explanation for every decision. +The application can use the answer distribution to choose an action or request further analysis. +These are intended uses, not guarantees of accuracy on every task. + +| Answer type | Meaning | Example | +|---|---|---| +| Binary (`noul` in the API) | Estimated probability of the supplied true condition | Does this proposed call satisfy the request? | +| Choice | Distribution over named options and the highest-scoring option | Which queue receives this ticket? | +| Ordered score | Distribution over rubric levels and their expected index | How well does this response satisfy the rubric? | + +## How the decision works + +The model keeps the original Dory architecture and vocabulary output head. +LoRA training changes attention and MLP weights. It adds no separate classification head. +The model uses **two recurrent loops**, which repeat internal computation through Dory layers without generating a chain of thought. + +The prompt combines the state, evaluation instructions, a question, and JSON-described options with single-token labels such as `A` and `B`. +This excerpt illustrates the question portion: + +```text +Question: Is the response supported by the supplied evidence? + +Options: +A: {"option":"true","description":"Every claim is supported by the evidence."} +B: {"option":"false","description":"At least one claim lacks support."} +``` + +The complete prompt also uses the Dory chat template, disabled explicit reasoning, and an assistant prefix of `Answer:\n`. +At that position, the runtime reads the next-token logits for the permitted labels. +It applies softmax across those labels and maps the resulting distribution to the requested answer type. +These are conditional probabilities over the supplied options. They do not establish that the option list covers every possible answer. + +The application enforces the response type and option membership. +The model supplies the judgement within that structure. +This training does not establish support for arbitrary JSON schemas or open-ended structured generation. + +## How Eider serves decisions + +Eider exposes a `/v1/decisions` endpoint. A request contains a shared state and one or more typed questions. +Each question supplies its permitted answers and their meanings. + +1. Eider renders the state and each question with the decision prompt described above. +2. It processes the shared state once and evaluates separate question branches. Questions cannot attend to each other. +3. It reads only the vocabulary-head rows for the answer labels at the end of each branch. +4. It applies softmax over those labels and returns the requested binary probability, choice distribution, or ordered score. + +The decision path generates no answer tokens or explanation. Dory still performs its two internal recurrent loops. +Eider batches question branches, reuses cached state prefixes, and uses a compact label readout to reduce repeated work. +These are serving optimizations. The weights contain the learned decision behaviour and need no Eider-specific classification head. +Another Dory runtime can reproduce the method with the same prompt, label mapping, recurrent settings, and probability calculation. + +A fitted calibration profile can adjust the distributions after inference, but this release does not include one. +The probabilities describe the supplied options; they are not independently verified confidence estimates. + +## Training data + +The adapter carries five training stages. The tables below list all source families, grouped by purpose. +A source can appear in several stages through replay. +Pinned versions, source-file hashes, recorded source licences, and split rules are in [training-data.json](training-data.json). +This repository contains model weights and metadata; they do not redistribute the source datasets. + +### Latest stage: state tracking, planning, and human judgements + +| Source | Questions in this stage | What the model learns | +|---|---:|---| +| [SCONE](https://nlp.stanford.edu/projects/scone/) — Alchemy and Tangrams | 6,000 | Track a world state through a complete instruction sequence and answer from the recorded final state. | +| Independent Eider planning generators, motivated by [PlanBench](https://github.com/karthikv792/LLMs-Planning/tree/main/plan-bench) | 8,000 | Choose optimal next moves and assess legality or progress in block stacking and package delivery. | +| [NVIDIA HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) | 2,000 | Learn correctness, helpfulness, coherence, and preference judgements from individual human ratings. | +| Replay of earlier action questions | 8,000 | Retain action-selection behaviour from the four environment sources below. | +| Replay of earlier general decision questions | 8,000 | Retain earlier decision, tool, document, reasoning, and code behaviour. | +| **Total** | **32,000** | **16,000 new questions and 16,000 replay questions.** | + +SCONE supplies recorded state labels; its Scene domain is excluded pending an alignment audit. +The planning generators and breadth-first search implementation are independent Eider code. +No published PlanBench instances or generator code were copied. +Planning targets share mass among equally optimal actions. +HelpSteer2 targets use individual rating and preference frequencies, preserving disagreement and ties. +Inputs exclude annotator ratings and justifications. + +### Earlier action and environment training + +These sources supplied the 24,000 new questions in the preceding 48,000-question stage; its other 24,000 questions were replay. + +| Source | New questions in that stage | Preparation | +|---|---:|---| +| [JAT BabyAI](https://huggingface.co/datasets/jat-project/jat-dataset) | 8,000 | Successful expert navigation, doors, keys, and object placement. Maps use past observations only. | +| [ALFWorld through Early Experience](https://huggingface.co/datasets/osunlp/early-experience) | 6,000 | Recorded expert commands and admissible actions, with the supplied observation history. | +| [Competitive Game Dataset](https://huggingface.co/datasets/yyyyyyjjjjzzz/competitive-game-dataset) — Connect Four | 4,000 | Solver-labelled legal moves, with equal target mass for tied best moves. | +| [ScienceWorld gold paths](https://github.com/allenai/ScienceWorld/tree/e8216d6044e8e39be9fcb185e3b2dfb602584b52/goldpaths) | 6,000 | Select among 17 operation types using the goal, room, inventory, and past results. | + +ScienceWorld training concerns operation types, not complete object-specific commands. +Expert imitation labels do not establish that every alternative action would fail. + +### General typed decisions and controlled scenarios + +| Source | Selected data and use | +|---|---| +| [Open-Jev](https://huggingface.co/datasets/ZefanCai/Open-Jev) | `release-v2-redistributable`: control, workflow, reasoning, and game questions. Original target distributions are preserved. | +| [SargeDev/jev-distill-corpus-v3](https://huggingface.co/datasets/SargeDev/jev-distill-corpus-v3) | Only `yuri_v3` rows: diverse decision questions with supplied probability targets. | +| [Procedural Typed Decisions](https://huggingface.co/datasets/tasksource/procedural-typed-decisions) | Rule-based and exact-probability tasks covering retrieval, state tracking, arithmetic, routing, and planning. | +| [Open-Jev-v1.1](https://huggingface.co/datasets/ZefanCai/Open-Jev-v1.1) | `community-hard-mix-v2-redistributable`: WANLI-derived inference and controlled tool, document, support, and rubric scenarios. | + +### Tools, documents, reading, mathematics, and code + +| Source | Preparation | +|---|---| +| [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) | Judge reference tool calls and deliberately invalid variants that violate the supplied argument schema. | +| [RAGTruth](https://github.com/ParticleMedia/RAGTruth) | Evidence-grounding questions from human span annotations, responses, and their supplied documents. | +| [RACE](https://huggingface.co/datasets/ehovy/race) | Reading-comprehension questions with original options and labels. | +| [GSM8K](https://huggingface.co/datasets/openai/gsm8k) | Numeric answer selection with generated distractors. Reference reasoning is excluded from the input. | +| [Devign through CodeXGLUE](https://huggingface.co/datasets/google/code_x_glue_cc_defect_detection) | Binary C-function defect questions using the source labels. | + +### Data separation and target meaning + +Complete source groups stay together: episodes, games, goals, conversations, documents, or prompts. +Planning groups include equivalent problems under block, package, and location renaming. +Public test examples never enter training, and previous evaluation reservations remain excluded. +New source groups contribute at most four questions each. +The latest stage rejects inputs above 1,152 tokens instead of truncating them. + +**No JevMan or Pac-Man states, layouts, trajectories, rewards, or labels are used.** +Other game and environment training is listed explicitly above. +These results do not measure full-episode game performance. + +Training targets have different meanings across sources: recorded answers, expert actions, optimal actions, human votes, or supplied distributions. +Optimal-action mass is not a win probability, and human vote frequencies do not establish outcome probabilities. + +## Training recipe and history + +Training starts from NVIDIA's BF16 revision `b366b87f762ea334ba3eb55912031baba1515810`. +Rank-8 LoRA with alpha 16 updates 16,220,160 adapter parameters across 288 attention and MLP projections. +The original weights remain frozen. Training and evaluation use two recurrent loops. +The loss is soft-target conditional cross-entropy plus 0.5 times Brier error over the permitted answers. + +| Stage | Training questions | Mixture | Learning rate | +|---|---:|---|---:| +| Initial decisions | 49,152 | 24,576 Open-Jev + 24,576 Yuri v3 | 0.0001 | +| Broader decisions | 65,536 | 24,576 procedural + 16,384 WANLI-derived + 8,192 controlled + 16,384 replay | 0.00002 | +| Tools and documents | 32,000 | 4,000 tools + 4,000 documents + 8,000 general + 16,000 replay | 0.00001 | +| Action selection | 48,000 | 24,000 environment questions + 24,000 replay | 0.00001 | +| **State tracking, planning, and judging** | **32,000** | **16,000 new + 16,000 replay** | **0.00001** | + +Replay repeats earlier training questions to retain previous behaviour. +These are questions processed per stage, not a total of unique examples. +The latest stage continues the previous adapter with a fresh optimizer. +It completed 8,000 updates of four questions each on one GB300, with about 45 minutes of training compute. + +## Accuracy evaluation + +The following results use **native Eider NVFP4 inference without probability calibration**. +The previous and new adapters use the same frozen Eider executable, prompt format, and two-loop configuration. +The parent NVFP4 export is byte-identical to the published `actions-48k` weights. + +Checkpoint selection uses 1,024 validation questions: 512 new questions and 512 earlier questions. +The criterion is lowest Brier error, then cross-entropy, then earliest step. +The unchanged parent and steps 2,000, 4,000, and 8,000 were eligible; step 8,000 won. +Validation accuracy improved from 60.06% to 76.17%, and Brier error fell from 0.5027 to 0.2779. +The 512 new test questions were scored after selection. + +| Question set | Model | Questions | Accuracy | Brier ↓ | Cross-entropy ↓ | +|---|---|---:|---:|---:|---:| +| New state, planning, and judging questions | Previous `actions-48k` NVFP4 | 512 | 46.48% | 0.7261 | 2.2838 | +| New state, planning, and judging questions | This `state-32k` NVFP4 | 512 | 75.59% | 0.2706 | 1.0355 | +| Earlier questions, reused | Previous `actions-48k` NVFP4 | 2,048 | 79.54% | 0.2521 | 0.7409 | +| Earlier questions, reused | This `state-32k` NVFP4 | 2,048 | 79.35% | 0.2642 | 0.7846 | +| General questions, reused | Previous `actions-48k` NVFP4 | 89 | 64.04% | 0.4997 | 1.0697 | +| General questions, reused | This `state-32k` NVFP4 | 89 | 65.17% | 0.4791 | 1.0044 | + +The new test breaks down as follows: + +| Source | Questions | Previous NVFP4 | This NVFP4 | Brier, previous → current | +|---|---:|---:|---:|---:| +| SCONE state tracking | 192 | 42.19% | 75.52% | 0.8904 → 0.3779 | +| Block-stacking and delivery planning | 192 | 48.44% | 92.19% | 0.6956 → 0.1909 | +| HelpSteer2 human judgements | 128 | 50.00% | 50.78% | 0.5253 → 0.2290 | + +State tracking and planning improve substantially. +HelpSteer2 accuracy changes by one answer; its predicted distributions move closer to the supplied human rating distributions. +Earlier-question accuracy changes by four answers out of 2,048, while Brier and cross-entropy become slightly worse. +The 89-question general check improves by one answer and is too small to establish broad generalization gains. + +Accuracy accepts any option with maximum target probability, including ties. +Brier and cross-entropy compare the full predicted and target distributions. +The earlier and general sets are reused development checks, not independent final benchmarks. +Converted questions do not follow the original datasets' complete benchmark protocols. + +**These scores apply to NVFP4.** Neither the matching BF16 merge nor the Q8_0 GGUF has received a separate full accuracy evaluation. +The original unfine-tuned NVIDIA model was not rescored in this comparison. +[Full results](evaluation/nvfp4-comparison.json) include every candidate, answer type, and source. +[Runtime metadata](evaluation/runtime.json) records the executable and script hashes. + +## Probability calibration + +No calibration profile has been fitted for `state-32k`. +The previous profile was removed from the current release because it belongs to different weights. +It remains in the original [NVFP4 repository’s `actions-48k` tag](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-nvfp4/tree/actions-48k) for reproducibility. +The 1,024-question calibration split remains separate from training, checkpoint selection, and the new test. +Uncalibrated softmax values are conditional scores over the permitted answers; they are not proven real-world outcome probabilities. + +## Portability and files + +Another engine can implement the same method using Dory, its tokenizer and prompt, two loops, and selected-label scoring. +Eider-specific CUDA kernels are not part of the learned weights. +Eider accelerates serving with shared state processing, independent question branches, batching, CUDA graphs, and selected output logits. +A different prompt, label mapping, loop count, or quantization can change the results. + +The root contains eight indexed BF16 safetensors shards, merged from the original NVIDIA weights and the selected adapter. +Every saved tensor was checked against its CPU merge result. These merged BF16 files have not received a separate accuracy pass. + +The matching standalone LoRA adapter is in `adapter/`. The complete NVFP4 package also includes a copy. +The adapter uses Eider tensor names rather than PEFT format; the supplied Python module loads that format. +The adapter applies to the original NVIDIA BF16 checkpoint. Do not apply it again to the merged release weights. +Its SHA-256 is `60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d`. + +The NVFP4 release quantizes our fine-tuned BF16 weights; it is not NVIDIA's original NVFP4 model. +Its export uses packed E2M1 weights, E4M3 block scales, an F32 tensor scale, and a ModelOpt-compatible layout. +The quantizer searches all 126 positive finite E4M3 scales for minimum block reconstruction error after BF16 merging. +[Export metadata](evaluation/export.json) records the recipe, source hashes, and per-projection statistics. + +## Use the GGUF with llama.cpp + +The GGUF works with llama.cpp's native `/v1/systemone` decision endpoint. No Eider server, custom llama.cpp patch, or further training is required. +**Dory architecture support is still in NVIDIA's unmerged [llama.cpp PR #30306](https://github.com/ggml-org/llama.cpp/pull/30306).** +Use that branch until architecture support reaches upstream. This requirement also applies to ordinary generation. + +### Download + +Install the Hugging Face CLI and authenticate with `hf auth login` if this repository is private. +Download only the format you need: + +```sh +hf download rdaum/eider-dory-1.0-4b-decisions \ + dory-decisions-Q8_0.gguf --local-dir ./dory-decisions +``` + +For NVFP4, use `--include 'nvfp4/*'` instead of the GGUF filename, then point your runtime at `./dory-decisions/nvfp4`. +For BF16, use `--include '*.json' '*.jinja' '*.safetensors' --exclude 'nvfp4/*' 'adapter/*'` instead of the GGUF filename. + +### Build with Dory support and CUDA + +These commands require Git, CMake, a C++ compiler, and the CUDA toolkit. +They build the Dory support branch for DGX Spark / GB10: + +```sh +git clone https://github.com/ggml-org/llama.cpp llama.cpp-dory +git -C llama.cpp-dory fetch origin pull/30306/head +git -C llama.cpp-dory checkout --detach FETCH_HEAD +cmake -S llama.cpp-dory -B llama.cpp-dory/build \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=121 \ + -DLLAMA_CURL=OFF \ + -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_SERVER=ON +cmake --build llama.cpp-dory/build --target llama-server -j 6 +``` + +For another NVIDIA GPU, select its CUDA architecture or omit `CMAKE_CUDA_ARCHITECTURES` for automatic detection. +The compatibility checks below used GB10. + +### Start the server + +```sh +./llama.cpp-dory/build/bin/llama-server \ + --model ./dory-decisions/dory-decisions-Q8_0.gguf \ + --alias dory-decisions \ + --host 127.0.0.1 --port 18091 \ + --ctx-size 4096 --parallel 1 \ + --threads 4 --threads-batch 4 \ + --batch-size 512 --ubatch-size 512 \ + --fit off --gpu-layers all --no-warmup +``` + +The command offloads all layers to the GPU. The 4,096-token context is a small working example, not the model's context limit. + +### Ask a typed question + +```sh +curl http://127.0.0.1:18091/v1/systemone \ + -H 'Content-Type: application/json' \ + -d '{"state":"The key is red.","questions":{"colour":{"type":"choice","instructions":"What colour is the key?","criteria":{"red":"Red","blue":"Blue"}}}}' +``` + +The endpoint returns answer probabilities with zero generated tokens. +The GGUF provides a named `systemone` prompt template and selects llama.cpp's existing `granite-switch` decision convention. +That name selects letter labels, true-before-false ordering, and selected next-token logits. **The architecture and weights are Dory, not IBM Granite.** +The normal chat template is also retained. + +| Question type | Supported input | +|---|---| +| `choice` | 2–64 named options with text or null descriptions. | +| `noul` | True/false, with optional text descriptions; defaults are `Yes` and `No`. | +| `score` | 2–10 ordered text descriptions. | + +This llama.cpp convention supports 10 score levels; Eider supports 64. +The tested server rejects excess score levels with HTTP 400. Template validation failures can return HTTP 500. +The template accepts text, JSON states, and plain text chat. Serialize tool-call history as state text. +Image and audio inputs are unsupported. + +### What was tested + +On 2026-10-21, the Q8_0 GGUF ran on GB10 with all 73 layers offloaded and no local llama.cpp source changes. +Eight native prompt checks matched Eider's token IDs and answer labels exactly. +They covered JSON, Unicode, chat, 64 choices, default yes/no descriptions, and 10 score levels. +The 24-request GPU smoke check covered all three answer types and returned the expected answers with zero generated tokens. + +For short requests with changing state, median latency was 62 ms for one question and 191 ms for three questions. +Each median used five timed requests. Server logs showed full prompt processing without prefix reuse. +These small compatibility checks do not establish Q8_0 accuracy on the full evaluation sets. +[GGUF provenance and test evidence](gguf/provenance.json) record the tested source revision, file hash, and checks. + +## Scope and provenance + +This experimental fine-tune targets typed decisions, action selection, state tracking, and planning. +The results do not establish better general chat, reliable decisions in every domain, or full-context quality. +[BF16 provenance](provenance.json), [NVFP4 provenance](nvfp4/provenance.json), and [format hashes](formats.json) bind the files to the selected adapter and source weights. + +NVIDIA supplies the base model, and the linked authors supply the training sources. +[Eider](https://github.com/rdaum/eider) supplies the inference implementation and experiment tooling. +The base model licence is [OpenMDW-1.1](LICENSE). This is an independent derivative. diff --git a/adapter/README.md b/adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad4c5efd9689844059e3a07cd8c5bbd07234ba90 --- /dev/null +++ b/adapter/README.md @@ -0,0 +1,15 @@ +# Eider Dory adapter + +This adapter uses Eider tensor names. It is not a PEFT adapter. +The complete model weights already include these changes. Do not apply the adapter twice. + +To load the adapter with the supplied Python module, use the original BF16 snapshot: + +```python +from pathlib import Path +from dory_lora import Dory + +model = Dory.load(Path("/path/to/original-bf16"), rank=8, alpha=16, loops=2) +model.load_adapter(Path("adapter.safetensors")) +model.eval() +``` diff --git a/adapter/adapter.safetensors b/adapter/adapter.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..0cc9db2b4559cb01a6e06cad9ed4a7078abd5bad --- /dev/null +++ b/adapter/adapter.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d +size 64941920 diff --git a/adapter/dory_lora.py b/adapter/dory_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..ce15bdfdddd78f4b4e5fb26dc1ab3fd6ead39cfc --- /dev/null +++ b/adapter/dory_lora.py @@ -0,0 +1,274 @@ +"""BF16 Dory with frozen checkpoint weights and trainable linear LoRA adapters. + +The nGPT equations follow the independent dory-checkpoint-reference.py oracle +and vLLM revision 55076950b6c155f384d6766d63563c3bc20a0e3f. This is a training +experiment, not a replacement for Eider's CUDA serving implementation. +""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import torch +from safetensors import safe_open +from safetensors.torch import load_file, save_file +from torch import nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint + + +def norm(x): + value = x.float() + return (value * torch.rsqrt(value.square().sum(-1, keepdim=True) + 1e-6)).to( + x.dtype + ) + + +class Linear(nn.Module): + def __init__(self, weight, rank, alpha): + super().__init__() + self.register_buffer("weight", weight) + self.scale = alpha / rank if rank else 0 + if rank: + self.lora_a = nn.Parameter( + torch.empty(rank, weight.shape[1], device=weight.device) + ) + self.lora_b = nn.Parameter( + torch.zeros(weight.shape[0], rank, device=weight.device) + ) + nn.init.kaiming_uniform_(self.lora_a, a=math.sqrt(5)) + else: + self.register_parameter("lora_a", None) + self.register_parameter("lora_b", None) + + def forward(self, x): + base = F.linear(x, self.weight) + if self.lora_a is None: + return base + delta = F.linear(F.linear(x, self.lora_a.to(x.dtype)), self.lora_b.to(x.dtype)) + return (base.float() + self.scale * delta.float()).to(x.dtype) + + +class Layer(nn.Module): + def __init__(self, config, index, read, rank, alpha): + super().__init__() + self.config = config + self.kind = config["layer_types"][index] + prefix = f"backbone.layers.{index}" + mixer = prefix + ".mixer" + std = config["initializer_range"] + if self.kind == "mlp": + names = ("gate_proj", "up_proj", "down_proj") + scale = math.sqrt(config["hidden_size"]) + self.register_buffer("suv_gate", read(mixer + ".suv_gate").float() * scale) + self.register_buffer("suv_up", read(mixer + ".suv_up").float() * scale) + rate = read(prefix + ".mlp_alpha") + else: + names = ("q_proj", "k_proj", "v_proj", "g_proj", "o_proj") + dim = config["head_dim"] + self.register_buffer( + "sqk", (read(mixer + ".sqk").float() * (math.sqrt(dim) / std)).square() + ) + g0 = config.get("attention_output_gate_logit_scale_init") or math.sqrt( + config["hidden_size"] + ) + self.register_buffer( + "s_gate", + (read(mixer + ".s_gate").float() * (math.sqrt(g0) / std)).square(), + ) + rate = read(prefix + ".attn_alpha") + self.register_buffer( + "rate", (rate.float() * config["ngpt_alpha_init"] / std).abs() + ) + self.projections = nn.ModuleDict( + { + name: Linear(read(mixer + "." + name + ".weight"), rank, alpha) + for name in names + } + ) + + def forward(self, h, rotary, sliding_mask, reference=False): + p, c, dtype = self.projections, self.config, h.dtype + if self.kind == "mlp": + gate = (p["gate_proj"](h).float() * self.suv_gate).to(dtype) + up = (p["up_proj"](h).float() * self.suv_up).to(dtype) + out = p["down_proj"](norm((F.silu(gate.float()) * up.float()).to(dtype))) + else: + nq, nk, dim = ( + c["num_attention_heads"], + c["num_key_value_heads"], + c["head_dim"], + ) + cos, sin = rotary[self.kind] + + def rope(x): + a, b = x.float().chunk(2, -1) + return torch.cat((a * cos - b * sin, b * cos + a * sin), -1).to(dtype) + + q = norm(rope(p["q_proj"](h).reshape(*h.shape[:-1], nq, dim))) + k = norm(rope(p["k_proj"](h).reshape(*h.shape[:-1], nk, dim))) + q = (q.float() * self.sqk.reshape(nq, dim)).to(dtype) + v = p["v_proj"](h).reshape(*h.shape[:-1], nk, dim) + # Repeat GQA heads explicitly so both SDPA backends and the oracle + # use the same layout. Batch rows retain independent attention. + q = q.transpose(1, 2) + k = k.repeat_interleave(nq // nk, 2).transpose(1, 2) + v = v.repeat_interleave(nq // nk, 2).transpose(1, 2) + mask = sliding_mask if self.kind == "sliding_attention" else None + if reference: + if mask is None: + mask = torch.ones( + h.shape[1], h.shape[1], device=h.device, dtype=torch.bool + ).tril() + scores = (q.float() @ k.float().transpose(-1, -2)) / math.sqrt(dim) + out = ( + scores.masked_fill(~mask, -torch.inf).softmax(-1) @ v.float() + ).to(dtype) + else: + out = F.scaled_dot_product_attention( + q, k, v, attn_mask=mask, is_causal=mask is None + ) + out = out.transpose(1, 2).reshape(*h.shape[:-1], nq * dim) + gate = p["g_proj"](h) + out = norm( + (out.float() * torch.sigmoid(gate.float() * self.s_gate)).to(dtype) + ) + out = p["o_proj"](out) + a, b = norm(h).float(), norm(out).float() + return norm(a + self.rate * (b - a)).to(dtype) + + +class Dory(nn.Module): + def __init__(self, config, read, *, rank=8, alpha=16, loops=2, checkpointing=False): + super().__init__() + if config.get("model_type") != "dory" or config.get("quantization_config"): + raise ValueError("requires an unquantized Dory checkpoint") + if loops < 1 or rank < 0 or alpha <= 0: + raise ValueError("invalid loop count or adapter dimensions") + self.config, self.loops, self.checkpointing = config, loops, checkpointing + self.register_buffer("embedding", read("backbone.embeddings.weight")) + self.register_buffer("head", read("lm_head.weight")) + self.register_buffer( + "head_scale", + read("lm_head.logit_scale").float() / config["initializer_range"], + ) + self.layers = nn.ModuleList( + Layer(config, i, read, rank, alpha) + for i in range(config["num_hidden_layers"]) + ) + + @classmethod + def load(cls, path: Path, *, device="cuda", **kwargs): + config = json.loads((path / "config.json").read_text()) + with safe_open( + path / "model.safetensors", framework="pt", device="cpu" + ) as weights: + consumed = set() + + def read(name): + value = weights.get_tensor(name) + if value.dtype != torch.bfloat16: + raise ValueError(f"expected BF16 weight: {name}") + consumed.add(name) + return value.to(device) + + model = cls(config, read, **kwargs) + if consumed != set(weights.keys()): + raise ValueError( + f"unconsumed checkpoint tensors: {set(weights.keys()) - consumed}" + ) + return model + + def forward(self, ids, labels=None, *, lengths=None, reference=False): + c = self.config + single = ids.ndim == 1 + if single: + ids = ids.unsqueeze(0) + positions = torch.arange(ids.shape[1], device=ids.device) + dim = c["head_dim"] + rotary = {} + for kind, params in c["rope_parameters"].items(): + angles = positions.float()[:, None] * params["rope_theta"] ** ( + -torch.arange(dim // 2, device=ids.device).float() / (dim // 2) + ) + rotary[kind] = (angles.cos()[:, None, :], angles.sin()[:, None, :]) + # For short training rows the sliding mask equals the causal mask. + sliding_mask = None + if ids.shape[1] > c["sliding_window"]: + distance = positions[:, None] - positions[None, :] + sliding_mask = (distance >= 0) & (distance < c["sliding_window"]) + + def apply(h, start, end): + for layer in self.layers[start:end]: + if self.training and self.checkpointing and not reference: + h = checkpoint(layer, h, rotary, sliding_mask, use_reentrant=False) + else: + # Keep the serial evaluator and oracle eager. Their batch-one, + # inference-only graphs otherwise compete with training graphs. + forward = ( + layer if self.training and not reference else layer.forward + ) + h = forward(h, rotary, sliding_mask, reference) + return h + + ni, nr = c["n_input_layers"], c["n_recurrent_layers"] + h = apply(F.embedding(ids, self.embedding), 0, ni) + input_h = h + for _ in range(self.loops): + h = norm(h + input_h) + h = norm(apply(h, ni, ni + nr)) + h = apply(h, ni + nr, len(self.layers)) + # Right-padding is always after each real query, so causal attention + # excludes it without a padding mask. Read only each row's final token. + last = ( + h[:, -1] + if lengths is None + else h[torch.arange(ids.shape[0], device=ids.device), lengths - 1] + ) + weight = self.head if labels is None else self.head[labels] + scale = self.head_scale if labels is None else self.head_scale[labels] + logits = ( + torch.bmm(weight, last.unsqueeze(-1)).squeeze(-1) + if labels is not None and labels.ndim == 2 + else F.linear(last, weight) + ).float() * scale + return logits.squeeze(0) if single else logits + + def compile_layers(self): + # Compile individual layers so recurrent traversals reuse their graphs. + # Keep the BF16 rounding points used by the eager nGPT equations. + # All layer kinds share Layer.forward's code cache. Attention masks, + # input gradients and shape guards produce more than eight variants when + # training batches cross the sliding-window boundary. Keep a bounded + # cache large enough for those variants; fullgraph raises on overflow. + torch._dynamo.config.recompile_limit = max( + torch._dynamo.config.recompile_limit, 32 + ) + for layer in self.layers: + layer.compile( + fullgraph=True, + dynamic=True, + options={"emulate_precision_casts": True}, + ) + + def save_adapter(self, path): + save_file( + { + name: p.detach().cpu().contiguous() + for name, p in self.named_parameters() + }, + str(path), + ) + + def load_adapter(self, path): + values = load_file(str(path)) + params = dict(self.named_parameters()) + if values.keys() != params.keys(): + raise ValueError("adapter does not match the model's projection modules") + with torch.no_grad(): + for name, p in params.items(): + if p.shape != values[name].shape: + raise ValueError(f"adapter shape mismatch: {name}") + p.copy_(values[name]) diff --git a/adapter/eider_adapter_config.json b/adapter/eider_adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..ca261c14ad38a3d6b9a53a72ed8720439459d017 --- /dev/null +++ b/adapter/eider_adapter_config.json @@ -0,0 +1,12 @@ +{ + "format": "eider.dory.lora/v1", + "base_model": "nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16", + "base_revision": "b366b87f762ea334ba3eb55912031baba1515810", + "rank": 8, + "alpha": 16.0, + "loops": 2, + "adapter_sha256": "60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d", + "tensor_names": "layers.{i}.projections.{name}.lora_a|lora_b", + "merge": "BF16(W + (alpha/rank) * F32(BF16(B)) @ F32(BF16(A)))", + "peft_compatible": false +} diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..7afbeb759a6786de17cde07485f9c2db387fd94d --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,195 @@ +{% macro render_extra_keys(json_dict, handled_keys) %} + {%- if json_dict is mapping %} + {%- for json_key in json_dict if json_key not in handled_keys %} + {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %} + {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }} + {%- else %} + {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }} + {%- endif %} + {%- endfor %} + {%- endif %} +{% endmacro %} +{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %} +{%- set medium_effort = medium_effort if medium_effort is defined else False %} +{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %} +{%- set ns = namespace(last_user_idx = -1) %} +{%- set loop_messages = messages %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if messages[0]["role"] == "system" %} + {%- set system_message = messages[0]["content"] %} + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_messages = messages %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = [] %} +{%- endif %} +{%- set ns = namespace(last_user_idx = -1) %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if system_message is defined %} + {{- "<|im_start|>system\n" + system_message }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- "<|im_start|>system\n" }} + {%- endif %} +{%- endif %} +{%- if tools is iterable and tools | length > 0 %} + {%- if system_message is defined and system_message | length > 0 %} + {{- "\n\n" }} + {%- endif %} + {{- "# Tools\n\nYou have access to the following functions:\n\n" }} + {{- "" }} + {%- for tool in tools %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{- "\n\n" ~ tool.name ~ "" }} + {%- if tool.description is defined %} + {{- '\n' ~ (tool.description | trim) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {{- '\n' }} + {{- '\n' ~ param_name ~ '' }} + {%- if param_fields.type is defined %} + {{- '\n' ~ (param_fields.type | string) ~ '' }} + {%- endif %} + {%- if param_fields.description is defined %} + {{- '\n' ~ (param_fields.description | trim) ~ '' }} + {%- endif %} + {%- if param_fields.enum is defined %} + {{- '\n' ~ (param_fields.enum | tojson | safe) ~ '' }} + {%- endif %} + {%- set handled_keys = ['name', 'type', 'description', 'enum'] %} + {{- render_extra_keys(param_fields, handled_keys) }} + {{- '\n' }} + {%- endfor %} + {%- endif %} + {% set handled_keys = ['type', 'properties', 'required'] %} + {{- render_extra_keys(tool.parameters, handled_keys) }} + {%- if tool.parameters is defined and tool.parameters.required is defined %} + {{- '\n' ~ (tool.parameters.required | tojson | safe) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} + {{- render_extra_keys(tool, handled_keys) }} + {{- '\n' }} + {%- endfor %} + {{- "\n" }} + {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }} +{%- endif %} +{%- if system_message is defined %} + {{- '<|im_end|>\n' }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in loop_messages %} + {%- if message.role == "assistant" %} + {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} + {%- set content = "\n" ~ message.reasoning_content ~ "" ~ (message.content | default('', true)) %} + {%- else %} + {%- set content = message.content | default('', true) %} + {%- if content is string -%} + {%- if '' not in content and '' not in content -%} + {%- set content = "" ~ content -%} + {%- endif -%} + {%- else -%} + {%- set content = content -%} + {%- endif -%} + {%- endif %} + {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %} + {{- '<|im_start|>assistant\n' }} + {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {%- if content is string and content | trim | length > 0 %} + {%- if include_content %} + {{- (content | trim) ~ '\n' -}} + {%- else %} + {%- set c = (content | string) %} + {%- if '' in c %} + {%- set c = c.split('')[-1] %} + {%- elif '' in c %} + {%- set c = c.split('')[0] %} + {%- endif %} + {%- set c = "" ~ c %} + {%- if c | length > 0 %} + {{- c ~ '\n' -}} + {%- endif %} + {%- endif %} + {%- else %} + {{- "" -}} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n' -}} + {%- if tool_call.arguments is defined %} + {%- for args_name, args_value in tool_call.arguments|items %} + {{- '\n' -}} + {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} + {{- args_value ~ '\n\n' -}} + {%- endfor %} + {%- endif %} + {{- '\n\n' -}} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- else %} + {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }} + {%- else %} + {%- set c = (content | default('', true) | string) %} + {%- if '' in c and '' in c %} + {%- set c = "" ~ c.split('')[-1] %} + {%- endif %} + {%- set c = c | trim %} + {%- if c | length > 0 %} + {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>assistant\n<|im_end|>\n' }} + {%- endif %} + {%- endif %} + {%- endif %} + {%- elif message.role == "user" or message.role == "system" %} + {{- '<|im_start|>' + message.role + '\n' }} + {%- set content = message.content | string %} + {%- if message.role == "user" and loop.index0 == ns.last_user_idx and medium_effort %} + {{- content + '\n\n{reasoning effort: efficient}' }} + {%- else %} + {{- content }} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.previtem and loop.previtem.role != "tool" %} + {{- '<|im_start|>user\n' }} + {%- endif %} + {{- '\n' }} + {{- message.content }} + {{- '\n\n' }} + {%- if not loop.last and loop.nextitem.role != "tool" %} + {{- '<|im_end|>\n' }} + {%- elif loop.last %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {%- if enable_thinking %} + {{- '<|im_start|>assistant\n\n' }} + {%- else %} + {{- '<|im_start|>assistant\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..e555e648bde2029e013d753b42d93509c1bdb265 --- /dev/null +++ b/config.json @@ -0,0 +1,113 @@ +{ + "architectures": [ + "DoryForCausalLM" + ], + "model_type": "dory", + "vocab_size": 131072, + "hidden_size": 2560, + "num_hidden_layers": 72, + "intermediate_size": 7168, + "num_attention_heads": 16, + "num_key_value_heads": 4, + "max_position_embeddings": 262144, + "tie_word_embeddings": false, + "ngpt_alpha_init": 0.05, + "attention_output_gate_logit_scale_init": null, + "mlp_bias": false, + "attention_bias": false, + "n_input_layers": 16, + "n_recurrent_layers": 40, + "n_output_layers": 16, + "n_recurrent_loops": 2, + "head_dim": 256, + "hidden_act": "silu", + "initializer_range": 0.0177, + "dtype": "bfloat16", + "sliding_window": 1024, + "rope_parameters": { + "sliding_attention": { + "rope_type": "default", + "rope_theta": 10000, + "partial_rotary_factor": 1.0 + }, + "full_attention": { + "rope_type": "default", + "rope_theta": 10000000, + "partial_rotary_factor": 1.0 + } + }, + "layer_types": [ + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "full_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "full_attention", + "mlp", + "sliding_attention", + "mlp", + 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0000000000000000000000000000000000000000..091a7f835db6055107e8be3dcd5b6aa6f9f064c8 --- /dev/null +++ b/dory-decisions-Q8_0.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb36b8ed263ce633649ccfad9c6e1dc065e9c3d333624fc27028f496089a38b8 +size 4232655616 diff --git a/evaluation/bf16-merge.json b/evaluation/bf16-merge.json new file mode 100644 index 0000000000000000000000000000000000000000..e5f4b5e73cff52ce1cca6f1dbefbd37535db6ce9 --- /dev/null +++ b/evaluation/bf16-merge.json @@ -0,0 +1,14 @@ +{ + "method": "W + (alpha/rank) * BF16(B) @ BF16(A), accumulated F32, stored BF16", + "device": "cpu", + "torch": "2.10.0+cu128", + "source_sha256": "207483664dfef54c4c4438863260d3ba423bf3d6009b43fe84264b281e9fda23", + "adapter_sha256": "60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d", + "quantizer_sha256": "baf5ad4237ff4390c41696a32ba97cb3b82ec87fe0bb78ea8878909f0a1b3772", + "projections": 288, + "tensor_count": 507, + "total_size": 7950458880, + 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this agreement, you accept and agree +to its terms. + +As used in this agreement, "Model Materials" means the materials provided to +you under this agreement, consisting of: (1) one or more machine learning +models (including architecture and parameters); and (2) all related artifacts +(including associated data, documentation and software) that are provided to +you hereunder. + +Subject to your compliance with this agreement, permission is hereby granted, +free of charge, to deal in the Model Materials without restriction, including +under all copyright, patent, database, and trade secret rights included or +embodied therein. + +If you distribute any portion of the Model Materials, you shall retain in your +distribution (1) a copy of this agreement, and (2) all copyright notices and +other notices of origin included in the Model Materials that are applicable to +your distribution. + +If you file, maintain, or voluntarily participate in a lawsuit against any +person or entity asserting that the Model Materials directly or indirectly +infringe any patent or copyright, then all rights and grants made to you +hereunder are terminated, unless that lawsuit was in response to a +corresponding lawsuit first brought against you. + +This agreement does not impose any restrictions or obligations with respect to +any use, modification, or sharing of any outputs generated by using the Model +Materials. + +THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE +ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL +TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW. + +YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY +APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION +ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL +MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS +REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE +DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR +ANYTHING INCORPORATED OR EMBODIED THEREIN. + +IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE +USE THEREOF OR OTHER DEALINGS THEREIN. diff --git a/nvfp4/NOTICE b/nvfp4/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..8df632d810cc59f4d371a68ab21f88f4c306a99f --- /dev/null +++ b/nvfp4/NOTICE @@ -0,0 +1,5 @@ +Derived from nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16, revision b366b87f762ea334ba3eb55912031baba1515810. +Original model: NVIDIA. Base model licence: OpenMDW-1.1. +Modifications: typed-decision LoRA fine-tuning, action selection, state tracking, planning, human-judgement training, BF16 merging, and NVFP4 quantization. +Training source attribution, pinned versions, and recorded source licences appear in README.md and training-data.json. +SCONE attribution: Long, Pasupat and Liang (2016). HelpSteer2 attribution: NVIDIA and Scale AI. diff --git a/nvfp4/adapter/README.md b/nvfp4/adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad4c5efd9689844059e3a07cd8c5bbd07234ba90 --- /dev/null +++ b/nvfp4/adapter/README.md @@ -0,0 +1,15 @@ +# Eider Dory adapter + +This adapter uses Eider tensor names. It is not a PEFT adapter. +The complete model weights already include these changes. Do not apply the adapter twice. + +To load the adapter with the supplied Python module, use the original BF16 snapshot: + +```python +from pathlib import Path +from dory_lora import Dory + +model = Dory.load(Path("/path/to/original-bf16"), rank=8, alpha=16, loops=2) +model.load_adapter(Path("adapter.safetensors")) +model.eval() +``` diff --git a/nvfp4/adapter/adapter.safetensors b/nvfp4/adapter/adapter.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..0cc9db2b4559cb01a6e06cad9ed4a7078abd5bad --- /dev/null +++ b/nvfp4/adapter/adapter.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d +size 64941920 diff --git a/nvfp4/adapter/dory_lora.py b/nvfp4/adapter/dory_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..ce15bdfdddd78f4b4e5fb26dc1ab3fd6ead39cfc --- /dev/null +++ b/nvfp4/adapter/dory_lora.py @@ -0,0 +1,274 @@ +"""BF16 Dory with frozen checkpoint weights and trainable linear LoRA adapters. + +The nGPT equations follow the independent dory-checkpoint-reference.py oracle +and vLLM revision 55076950b6c155f384d6766d63563c3bc20a0e3f. This is a training +experiment, not a replacement for Eider's CUDA serving implementation. +""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import torch +from safetensors import safe_open +from safetensors.torch import load_file, save_file +from torch import nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint + + +def norm(x): + value = x.float() + return (value * torch.rsqrt(value.square().sum(-1, keepdim=True) + 1e-6)).to( + x.dtype + ) + + +class Linear(nn.Module): + def __init__(self, weight, rank, alpha): + super().__init__() + self.register_buffer("weight", weight) + self.scale = alpha / rank if rank else 0 + if rank: + self.lora_a = nn.Parameter( + torch.empty(rank, weight.shape[1], device=weight.device) + ) + self.lora_b = nn.Parameter( + torch.zeros(weight.shape[0], rank, device=weight.device) + ) + nn.init.kaiming_uniform_(self.lora_a, a=math.sqrt(5)) + else: + self.register_parameter("lora_a", None) + self.register_parameter("lora_b", None) + + def forward(self, x): + base = F.linear(x, self.weight) + if self.lora_a is None: + return base + delta = F.linear(F.linear(x, self.lora_a.to(x.dtype)), self.lora_b.to(x.dtype)) + return (base.float() + self.scale * delta.float()).to(x.dtype) + + +class Layer(nn.Module): + def __init__(self, config, index, read, rank, alpha): + super().__init__() + self.config = config + self.kind = config["layer_types"][index] + prefix = f"backbone.layers.{index}" + mixer = prefix + ".mixer" + std = config["initializer_range"] + if self.kind == "mlp": + names = ("gate_proj", "up_proj", "down_proj") + scale = math.sqrt(config["hidden_size"]) + self.register_buffer("suv_gate", read(mixer + ".suv_gate").float() * scale) + self.register_buffer("suv_up", read(mixer + ".suv_up").float() * scale) + rate = read(prefix + ".mlp_alpha") + else: + names = ("q_proj", "k_proj", "v_proj", "g_proj", "o_proj") + dim = config["head_dim"] + self.register_buffer( + "sqk", (read(mixer + ".sqk").float() * (math.sqrt(dim) / std)).square() + ) + g0 = config.get("attention_output_gate_logit_scale_init") or math.sqrt( + config["hidden_size"] + ) + self.register_buffer( + "s_gate", + (read(mixer + ".s_gate").float() * (math.sqrt(g0) / std)).square(), + ) + rate = read(prefix + ".attn_alpha") + self.register_buffer( + "rate", (rate.float() * config["ngpt_alpha_init"] / std).abs() + ) + self.projections = nn.ModuleDict( + { + name: Linear(read(mixer + "." + name + ".weight"), rank, alpha) + for name in names + } + ) + + def forward(self, h, rotary, sliding_mask, reference=False): + p, c, dtype = self.projections, self.config, h.dtype + if self.kind == "mlp": + gate = (p["gate_proj"](h).float() * self.suv_gate).to(dtype) + up = (p["up_proj"](h).float() * self.suv_up).to(dtype) + out = p["down_proj"](norm((F.silu(gate.float()) * up.float()).to(dtype))) + else: + nq, nk, dim = ( + c["num_attention_heads"], + c["num_key_value_heads"], + c["head_dim"], + ) + cos, sin = rotary[self.kind] + + def rope(x): + a, b = x.float().chunk(2, -1) + return torch.cat((a * cos - b * sin, b * cos + a * sin), -1).to(dtype) + + q = norm(rope(p["q_proj"](h).reshape(*h.shape[:-1], nq, dim))) + k = norm(rope(p["k_proj"](h).reshape(*h.shape[:-1], nk, dim))) + q = (q.float() * self.sqk.reshape(nq, dim)).to(dtype) + v = p["v_proj"](h).reshape(*h.shape[:-1], nk, dim) + # Repeat GQA heads explicitly so both SDPA backends and the oracle + # use the same layout. Batch rows retain independent attention. + q = q.transpose(1, 2) + k = k.repeat_interleave(nq // nk, 2).transpose(1, 2) + v = v.repeat_interleave(nq // nk, 2).transpose(1, 2) + mask = sliding_mask if self.kind == "sliding_attention" else None + if reference: + if mask is None: + mask = torch.ones( + h.shape[1], h.shape[1], device=h.device, dtype=torch.bool + ).tril() + scores = (q.float() @ k.float().transpose(-1, -2)) / math.sqrt(dim) + out = ( + scores.masked_fill(~mask, -torch.inf).softmax(-1) @ v.float() + ).to(dtype) + else: + out = F.scaled_dot_product_attention( + q, k, v, attn_mask=mask, is_causal=mask is None + ) + out = out.transpose(1, 2).reshape(*h.shape[:-1], nq * dim) + gate = p["g_proj"](h) + out = norm( + (out.float() * torch.sigmoid(gate.float() * self.s_gate)).to(dtype) + ) + out = p["o_proj"](out) + a, b = norm(h).float(), norm(out).float() + return norm(a + self.rate * (b - a)).to(dtype) + + +class Dory(nn.Module): + def __init__(self, config, read, *, rank=8, alpha=16, loops=2, checkpointing=False): + super().__init__() + if config.get("model_type") != "dory" or config.get("quantization_config"): + raise ValueError("requires an unquantized Dory checkpoint") + if loops < 1 or rank < 0 or alpha <= 0: + raise ValueError("invalid loop count or adapter dimensions") + self.config, self.loops, self.checkpointing = config, loops, checkpointing + self.register_buffer("embedding", read("backbone.embeddings.weight")) + self.register_buffer("head", read("lm_head.weight")) + self.register_buffer( + "head_scale", + read("lm_head.logit_scale").float() / config["initializer_range"], + ) + self.layers = nn.ModuleList( + Layer(config, i, read, rank, alpha) + for i in range(config["num_hidden_layers"]) + ) + + @classmethod + def load(cls, path: Path, *, device="cuda", **kwargs): + config = json.loads((path / "config.json").read_text()) + with safe_open( + path / "model.safetensors", framework="pt", device="cpu" + ) as weights: + consumed = set() + + def read(name): + value = weights.get_tensor(name) + if value.dtype != torch.bfloat16: + raise ValueError(f"expected BF16 weight: {name}") + consumed.add(name) + return value.to(device) + + model = cls(config, read, **kwargs) + if consumed != set(weights.keys()): + raise ValueError( + f"unconsumed checkpoint tensors: {set(weights.keys()) - consumed}" + ) + return model + + def forward(self, ids, labels=None, *, lengths=None, reference=False): + c = self.config + single = ids.ndim == 1 + if single: + ids = ids.unsqueeze(0) + positions = torch.arange(ids.shape[1], device=ids.device) + dim = c["head_dim"] + rotary = {} + for kind, params in c["rope_parameters"].items(): + angles = positions.float()[:, None] * params["rope_theta"] ** ( + -torch.arange(dim // 2, device=ids.device).float() / (dim // 2) + ) + rotary[kind] = (angles.cos()[:, None, :], angles.sin()[:, None, :]) + # For short training rows the sliding mask equals the causal mask. + sliding_mask = None + if ids.shape[1] > c["sliding_window"]: + distance = positions[:, None] - positions[None, :] + sliding_mask = (distance >= 0) & (distance < c["sliding_window"]) + + def apply(h, start, end): + for layer in self.layers[start:end]: + if self.training and self.checkpointing and not reference: + h = checkpoint(layer, h, rotary, sliding_mask, use_reentrant=False) + else: + # Keep the serial evaluator and oracle eager. Their batch-one, + # inference-only graphs otherwise compete with training graphs. + forward = ( + layer if self.training and not reference else layer.forward + ) + h = forward(h, rotary, sliding_mask, reference) + return h + + ni, nr = c["n_input_layers"], c["n_recurrent_layers"] + h = apply(F.embedding(ids, self.embedding), 0, ni) + input_h = h + for _ in range(self.loops): + h = norm(h + input_h) + h = norm(apply(h, ni, ni + nr)) + h = apply(h, ni + nr, len(self.layers)) + # Right-padding is always after each real query, so causal attention + # excludes it without a padding mask. Read only each row's final token. + last = ( + h[:, -1] + if lengths is None + else h[torch.arange(ids.shape[0], device=ids.device), lengths - 1] + ) + weight = self.head if labels is None else self.head[labels] + scale = self.head_scale if labels is None else self.head_scale[labels] + logits = ( + torch.bmm(weight, last.unsqueeze(-1)).squeeze(-1) + if labels is not None and labels.ndim == 2 + else F.linear(last, weight) + ).float() * scale + return logits.squeeze(0) if single else logits + + def compile_layers(self): + # Compile individual layers so recurrent traversals reuse their graphs. + # Keep the BF16 rounding points used by the eager nGPT equations. + # All layer kinds share Layer.forward's code cache. Attention masks, + # input gradients and shape guards produce more than eight variants when + # training batches cross the sliding-window boundary. Keep a bounded + # cache large enough for those variants; fullgraph raises on overflow. + torch._dynamo.config.recompile_limit = max( + torch._dynamo.config.recompile_limit, 32 + ) + for layer in self.layers: + layer.compile( + fullgraph=True, + dynamic=True, + options={"emulate_precision_casts": True}, + ) + + def save_adapter(self, path): + save_file( + { + name: p.detach().cpu().contiguous() + for name, p in self.named_parameters() + }, + str(path), + ) + + def load_adapter(self, path): + values = load_file(str(path)) + params = dict(self.named_parameters()) + if values.keys() != params.keys(): + raise ValueError("adapter does not match the model's projection modules") + with torch.no_grad(): + for name, p in params.items(): + if p.shape != values[name].shape: + raise ValueError(f"adapter shape mismatch: {name}") + p.copy_(values[name]) diff --git a/nvfp4/adapter/eider_adapter_config.json b/nvfp4/adapter/eider_adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..ca261c14ad38a3d6b9a53a72ed8720439459d017 --- /dev/null +++ b/nvfp4/adapter/eider_adapter_config.json @@ -0,0 +1,12 @@ +{ + "format": "eider.dory.lora/v1", + "base_model": "nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16", + "base_revision": "b366b87f762ea334ba3eb55912031baba1515810", + "rank": 8, + "alpha": 16.0, + "loops": 2, + "adapter_sha256": "60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d", + "tensor_names": "layers.{i}.projections.{name}.lora_a|lora_b", + "merge": "BF16(W + (alpha/rank) * F32(BF16(B)) @ F32(BF16(A)))", + "peft_compatible": false +} diff --git a/nvfp4/chat_template.jinja b/nvfp4/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..7afbeb759a6786de17cde07485f9c2db387fd94d --- /dev/null +++ b/nvfp4/chat_template.jinja @@ -0,0 +1,195 @@ +{% macro render_extra_keys(json_dict, handled_keys) %} + {%- if json_dict is mapping %} + {%- for json_key in json_dict if json_key not in handled_keys %} + {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %} + {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }} + {%- else %} + {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }} + {%- endif %} + {%- endfor %} + {%- endif %} +{% endmacro %} +{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %} +{%- set medium_effort = medium_effort if medium_effort is defined else False %} +{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %} +{%- set ns = namespace(last_user_idx = -1) %} +{%- set loop_messages = messages %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if messages[0]["role"] == "system" %} + {%- set system_message = messages[0]["content"] %} + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_messages = messages %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = [] %} +{%- endif %} +{%- set ns = namespace(last_user_idx = -1) %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if system_message is defined %} + {{- "<|im_start|>system\n" + system_message }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- "<|im_start|>system\n" }} + {%- endif %} +{%- endif %} +{%- if tools is iterable and tools | length > 0 %} + {%- if system_message is defined and system_message | length > 0 %} + {{- "\n\n" }} + {%- endif %} + {{- "# Tools\n\nYou have access to the following functions:\n\n" }} + {{- "" }} + {%- for tool in tools %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{- "\n\n" ~ tool.name ~ "" }} + {%- if tool.description is defined %} + {{- '\n' ~ (tool.description | trim) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {{- '\n' }} + {{- '\n' ~ param_name ~ '' }} + {%- if param_fields.type is defined %} + {{- '\n' ~ (param_fields.type | string) ~ '' }} + {%- endif %} + {%- if param_fields.description is defined %} + {{- '\n' ~ (param_fields.description | trim) ~ '' }} + {%- endif %} + {%- if param_fields.enum is defined %} + {{- '\n' ~ (param_fields.enum | tojson | safe) ~ '' }} + {%- endif %} + {%- set handled_keys = ['name', 'type', 'description', 'enum'] %} + {{- render_extra_keys(param_fields, handled_keys) }} + {{- '\n' }} + {%- endfor %} + {%- endif %} + {% set handled_keys = ['type', 'properties', 'required'] %} + {{- render_extra_keys(tool.parameters, handled_keys) }} + {%- if tool.parameters is defined and tool.parameters.required is defined %} + {{- '\n' ~ (tool.parameters.required | tojson | safe) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} + {{- render_extra_keys(tool, handled_keys) }} + {{- '\n' }} + {%- endfor %} + {{- "\n" }} + {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }} +{%- endif %} +{%- if system_message is defined %} + {{- '<|im_end|>\n' }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- '<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in loop_messages %} + {%- if message.role == "assistant" %} + {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} + {%- set content = "\n" ~ message.reasoning_content ~ "" ~ (message.content | default('', true)) %} + {%- else %} + {%- set content = message.content | default('', true) %} + {%- if content is string -%} + {%- if '' not in content and '' not in content -%} + {%- set content = "" ~ content -%} + {%- endif -%} + {%- else -%} + {%- set content = content -%} + {%- endif -%} + {%- endif %} + {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %} + {{- '<|im_start|>assistant\n' }} + {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {%- if content is string and content | trim | length > 0 %} + {%- if include_content %} + {{- (content | trim) ~ '\n' -}} + {%- else %} + {%- set c = (content | string) %} + {%- if '' in c %} + {%- set c = c.split('')[-1] %} + {%- elif '' in c %} + {%- set c = c.split('')[0] %} + {%- endif %} + {%- set c = "" ~ c %} + {%- if c | length > 0 %} + {{- c ~ '\n' -}} + {%- endif %} + {%- endif %} + {%- else %} + {{- "" -}} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n' -}} + {%- if tool_call.arguments is defined %} + {%- for args_name, args_value in tool_call.arguments|items %} + {{- '\n' -}} + {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} + {{- args_value ~ '\n\n' -}} + {%- endfor %} + {%- endif %} + {{- '\n\n' -}} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- else %} + {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }} + {%- else %} + {%- set c = (content | default('', true) | string) %} + {%- if '' in c and '' in c %} + {%- set c = "" ~ c.split('')[-1] %} + {%- endif %} + {%- set c = c | trim %} + {%- if c | length > 0 %} + {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>assistant\n<|im_end|>\n' }} + {%- endif %} + {%- endif %} + {%- endif %} + {%- elif message.role == "user" or message.role == "system" %} + {{- '<|im_start|>' + message.role + '\n' }} + {%- set content = message.content | string %} + {%- if message.role == "user" and loop.index0 == ns.last_user_idx and medium_effort %} + {{- content + '\n\n{reasoning effort: efficient}' }} + {%- else %} + {{- content }} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if loop.previtem and loop.previtem.role != "tool" %} + {{- '<|im_start|>user\n' }} + {%- endif %} + {{- '\n' }} + {{- message.content }} + {{- '\n\n' }} + {%- if not loop.last and loop.nextitem.role != "tool" %} + {{- '<|im_end|>\n' }} + {%- elif loop.last %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {%- if enable_thinking %} + {{- '<|im_start|>assistant\n\n' }} + {%- else %} + {{- '<|im_start|>assistant\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/nvfp4/config.json b/nvfp4/config.json new file mode 100644 index 0000000000000000000000000000000000000000..4b1eb4d424db102bac53d1ed300ceb08734a1dcb --- /dev/null +++ b/nvfp4/config.json @@ -0,0 +1,138 @@ +{ + "architectures": [ + "DoryForCausalLM" + ], + "model_type": "dory", + "vocab_size": 131072, + "hidden_size": 2560, + "num_hidden_layers": 72, + "intermediate_size": 7168, + "num_attention_heads": 16, + "num_key_value_heads": 4, + "max_position_embeddings": 262144, + "tie_word_embeddings": false, + "ngpt_alpha_init": 0.05, + "attention_output_gate_logit_scale_init": null, + "mlp_bias": false, + "attention_bias": false, + "n_input_layers": 16, + "n_recurrent_layers": 40, + "n_output_layers": 16, + "n_recurrent_loops": 2, + "head_dim": 256, + "hidden_act": "silu", + "initializer_range": 0.0177, + "dtype": "bfloat16", + "sliding_window": 1024, + "rope_parameters": { + "sliding_attention": { + "rope_type": "default", + "rope_theta": 10000, + "partial_rotary_factor": 1.0 + }, + "full_attention": { + "rope_type": "default", + "rope_theta": 10000000, + "partial_rotary_factor": 1.0 + } + }, + "layer_types": [ + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "full_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "sliding_attention", + "mlp", + "full_attention", + "mlp", + 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"type": "float", + "group_size": 16 + }, + "targets": [ + "Linear" + ] + } + }, + "ignore": [ + "backbone.embeddings", + "lm_head" + ], + "quant_algo": "W4A16_NVFP4", + "quant_method": "modelopt", + "producer": { + "name": "eider-mse", + "version": "1" + } + } +} diff --git a/nvfp4/evaluation/export.json b/nvfp4/evaluation/export.json new file mode 100644 index 0000000000000000000000000000000000000000..69dd05ecc4992a3daf557127efdc8887f5b06151 --- /dev/null +++ b/nvfp4/evaluation/export.json @@ -0,0 +1,6646 @@ +{ + "run_dir": "art_3c5dbfc942580099ea75e8095e26ca25", + "source": "model", + "adapter_sha256": "60723ac470780ac7e8bfe7785c23665be2d5c5d6653cbd2fad653523214aea7d", + "source_sha256": "207483664dfef54c4c4438863260d3ba423bf3d6009b43fe84264b281e9fda23", + "checkpoint_sha256": { + "nvfp4-base": "602a1bc6a69c1a5c41253df785f335cea77eeeb854b985ab11c125bf1d08a547", + "nvfp4-merged": "9bc834f4dce29a48499b6614c11772d189710138b29c637a384f753b1326b4c0" + }, + "loops": 2, + "recipe": "mse", + 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