Replace adapter with the merged full-weight OBLIQ-IR-3B model (EMNLP 2026)

LICENSE_nvidia_base_model.txt

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NOTICE.txt

@@ -0,0 +1 @@
+Meta Llama 3.2 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

README.md

@@ -1,7 +1,7 @@
 ---
 license: cc-by-nc-4.0
-library_name: peft
 base_model: nvidia/llama-nv-embed-reasoning-3b
+library_name: sentence-transformers
 pipeline_tag: feature-extraction
 tags:
   - sentence-transformers
@@ -10,7 +10,6 @@ tags:
   - oblique-retrieval
   - authorship
   - stylometry
-  - lora
 language:
   - en
 ---
@@ -18,38 +17,19 @@ language:
 # OBLIQ-IR-3B
 
 A 3B single-vector dense retriever for **oblique retrieval** — queries whose relevance is decided by a
-latent attribute (implicit stance, abstract proof strategy, authorial style, a lossy recollection of a
-rhetorical exchange) that has little or no surface expression in the document.
+latent attribute (an implicit stance, an abstract proof strategy, an authorial style, a lossy recollection
+of a rhetorical exchange) that has little or no surface expression in the document.
 
-This is the model from *OBLIQ-IR: Training a Dense Retriever for Oblique Queries* (EMNLP 2026).
+From *OBLIQ-IR: Training a Dense Retriever for Oblique Queries* (EMNLP 2026).
 
-> ⚠️ **This repository contains a LoRA adapter, not merged weights.** You need the base model
-> [`nvidia/llama-nv-embed-reasoning-3b`](https://huggingface.co/nvidia/llama-nv-embed-reasoning-3b)
-> as well. Loading with `SentenceTransformer` handles this automatically (see below).
+**This repository holds the merged full-weight model** — load it and go, no base download, no PEFT.
 
-- 📊 Data, training mixture and all run files: [`DataScience-UIBK/OBLIQ-IR-Data`](https://huggingface.co/datasets/DataScience-UIBK/OBLIQ-IR-Data)
-- 🧪 Benchmark: [`dianetc/OBLIQ-Bench`](https://huggingface.co/datasets/dianetc/OBLIQ-Bench)
-
-## Results
-
-NDCG@10 (Gold) on OBLIQ-Bench. **Bold** = best non-oracle.
-
-| Pipeline | Writing | Math | Twitter | Congress |
-|---|---|---|---|---|
-| BM25 | .077 | .022 | .000 | .000 |
-| Qwen3-Embed-4B | .033 | .095 | .032 | .040 |
-| Gemini-2-Embedding | .164 | .144 | .068 | .059 |
-| GPT-5.2 Multi-Hop Agent | .061 | .161 | .141 | .183 |
-| OBLIQ-IR (no distillation) | .096 | .148 | .158 | .196 |
-| OBLIQ-IR (dense, this model) | **.211** | .140 | .151 | .187 |
-| **OBLIQ-IR (full = dense + reranker)** | **.211** | **.171** | **.177** | **.281** |
-| *Oracle GPT-5.2 Tournament (not attainable)* | *.515* | *.279* | *.331* | *.913* |
-
-"Full" applies TourRank (Y=5, Qwen3.6-27B) on Math/Twitter/Congress; for Writing the selected policy is
-the identity, so **the numbers above are what this checkpoint produces on its own for Writing**.
-
-Gains are large and significant on Writing (+0.116) and Congress (+0.085, both *p*<0.001), and marginal
-on Math (*p*=0.08) and Twitter (*p*=0.06). We do not claim the fifth OBLIQ-Bench task, WildChat-Errors.
+| | |
+|---|---|
+| 🧩 LoRA adapter only (48 MB) | [`DataScience-UIBK/OBLIQ-IR-3B-LoRA`](https://huggingface.co/DataScience-UIBK/OBLIQ-IR-3B-LoRA) |
+| 📊 Training data, runs, results | [`DataScience-UIBK/OBLIQ-IR-Data`](https://huggingface.co/datasets/DataScience-UIBK/OBLIQ-IR-Data) |
+| 💻 Code | [github.com/DataScience-UIBK/obliq-ir](https://github.com/DataScience-UIBK/obliq-ir) |
+| 🧪 Benchmark | [`dianetc/OBLIQ-Bench`](https://huggingface.co/datasets/dianetc/OBLIQ-Bench) |
 
 ## Usage
 
@@ -58,83 +38,101 @@ from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer("DataScience-UIBK/OBLIQ-IR-3B", trust_remote_code=True)
 
-queries   = ["A passage written with the same restless, aphoristic voice as this one."]
-documents = ["...your corpus documents..."]
-
-q = model.encode(["query: "   + x for x in queries])
-d = model.encode(["passage: " + x for x in documents])
-
+q = model.encode(["query: "   + "A passage in the same restless, aphoristic voice as this one."])
+d = model.encode(["passage: " + doc for doc in corpus])
 scores = model.similarity(q, d)
 ```
 
-**The `query: ` and `passage: ` prefixes are required** — the model was trained with them, and dropping
-them changes the embedding. Queries take no other prefix.
+**The `query: ` and `passage: ` prefixes are required** — the model was trained with them.
 
-> **Note on task instructions.** The training file carries a per-task instruction string alongside each
-> query, but the training script consumes only the query text, and the evaluation that produced every
-> number in the paper encodes the raw benchmark query with the `query: ` prefix alone. **Do not prepend a
-> task instruction** — the released checkpoint was neither trained nor evaluated with one, so adding one
-> moves you off-distribution. Task identity is carried by the mixture the model was trained on, not by a
-> runtime prefix.
+> **Do not prepend a task instruction.** The training file carries a per-task instruction string next to each
+> query, but the training script consumes only the query text, and the evaluation behind every number in the
+> paper encodes the raw query with the `query: ` prefix alone. Adding an instruction moves you
+> off-distribution. Task identity comes from the training mixture, not a runtime prefix.
 
-Truncate documents at 1024 tokens and queries at 256 to match the paper's evaluation:
+To match the paper's evaluation, encode documents at 1024 tokens and queries at 256:
 
 ```python
-model.max_seq_length = 1024   # documents
-d = model.encode(["passage: " + x for x in documents])
-model.max_seq_length = 256    # queries
-q = model.encode(["query: "   + x for x in queries])
+model.max_seq_length = 1024
+d = model.encode(["passage: " + x for x in corpus])
+model.max_seq_length = 256
+q = model.encode(["query: " + x for x in queries])
 ```
 
-## How it was trained
+## Results
 
-Two training-time signals, mixed into one encoder shared by all tasks:
+NDCG@10 Gold on OBLIQ-Bench. "Full" adds TourRank (Y=5) on Math/Twitter/Congress; on Writing the selected
+policy is the identity, so this model alone *is* the full system there.
 
-1. **Per-mechanism synthetic queries.** A document is read by an instruction LM (Qwen3.6-27B) which first
-   writes a description of the task's latent attribute, then writes a query through one of three lenses
-   matched to the OBLIQ query mechanisms — *descriptive* (Twitter), *analogue* (Math, Writing), or
-   *tip-of-the-tongue* (Congress). Four BM25 hard negatives are mined per query.
-2. **kNN-graph distillation from a frozen authorship encoder** (writing task only). A frozen authorship
-   encoder ([Rivera-Soto et al., 2021](https://huggingface.co/rrivera1849/LUAR-MUD)) defines a k=3 nearest-neighbour
-   graph over the writing corpus; its **edges** become anchor–positive training pairs. Only the topology is
-   used — the teacher's similarity *scores* are discarded. This transfers a style-versus-topic inductive
-   bias that no topical lens can express, lifting Writing from .096 to .211.
+| Pipeline | Writing | Math | Twitter | Congress |
+|---|---|---|---|---|
+| BM25 | .077 | .022 | .000 | .000 |
+| Qwen3-Embed-4B | .033 | .095 | .032 | .040 |
+| Gemini-2-Embedding | .164 | .144 | .068 | .059 |
+| GPT-5.2 Multi-Hop Agent | .061 | .161 | .141 | .183 |
+| OBLIQ-IR (no distillation) | .096 | .148 | .158 | .196 |
+| OBLIQ-IR dense | **.211** | .140 | .151 | .187 |
+| **OBLIQ-IR full** | **.211** | **.171** | **.177** | **.281** |
+| *Oracle GPT-5.2 Tournament (not attainable)* | *.515* | *.279* | *.331* | *.913* |
+
+Gains are large and significant on Writing (+0.116) and Congress (+0.085, both *p*<0.001) and marginal on
+Math (*p*=0.08) and Twitter (*p*=0.06). We do not claim the fifth task, WildChat-Errors.
 
-Neither stage touches the OBLIQ qrels.
+## Merged vs. adapter: a small numerical caveat
+
+The published numbers were produced by the **adapter** path (base weights + LoRA computed separately). This
+repository stores `W + BA·scaling` folded into bf16 weights. Because bf16 carries only about three
+significant digits, folding is slightly lossy, so this model does not reproduce the adapter bit-for-bit:
+
+| Task | Metric | This merged model | Adapter (paper) | Δ |
+|---|---|---|---|---|
+| Math | NDCG@10 | 0.1407 | 0.1403 | +0.0004 |
+| Twitter | NDCG@10 | 0.1516 | 0.1508 | +0.0008 |
+
+Top-1 results agree on 151/151 math queries and 275/281 twitter queries; deeper in the ranking the order
+shifts slightly. The differences are far below run-to-run variance, but they can move the **third decimal**.
+
+**For exact reproduction of the paper's tables, use
+[`OBLIQ-IR-3B-LoRA`](https://huggingface.co/DataScience-UIBK/OBLIQ-IR-3B-LoRA)**, which regenerates the
+published rankings bit-for-bit. Use this repository when you want convenience.
+
+## Training
+
+Two signals mixed into one encoder shared by all tasks. **(1) Per-mechanism synthetic queries:** an
+instruction LM describes a document's latent attribute, then writes a query through one of three lenses
+matched to the OBLIQ query mechanisms — descriptive, analogue, tip-of-the-tongue — with four BM25 hard
+negatives each. **(2) kNN-graph distillation from a frozen authorship encoder** (writing only): the k=3
+nearest-neighbour graph of [Rivera-Soto et al. (2021)](https://huggingface.co/rrivera1849/LUAR-MUD) over the
+writing corpus supplies anchor–positive pairs. Only the graph's *topology* is used; the teacher's similarity
+scores are discarded. That lifts Writing from .096 to .211. Neither stage touches the OBLIQ qrels.
 
 | Setting | Value |
 |---|---|
-| Base model | `nvidia/llama-nv-embed-reasoning-3b` (Llama-3.2-3B bi-encoder) |
-| Adapter | LoRA r=16, α=32, dropout 0.05, on q/k/v/o/gate/up/down (~24M params) |
-| Pooling | mean over token embeddings, then L2 normalise → 3072-dim |
-| Loss | CachedMultipleNegativesRankingLoss (Sentence-Transformers) |
-| Temperature | τ = 1/0.02 |
-| LR / schedule | 2e-4, 3% warmup, **linear** decay |
-| Batch | 4 per device × 40 GPUs × 2 grad-accum = **320** anchors |
-| Seq length | 1024 (queries truncated to 256 when encoding for evaluation) |
-| Epochs / hardware | 1 epoch, 40× H100, ~50 min |
-| Training rows | 154,361 (= 149,361 synthetic + 5,000 authorship kNN pairs) |
-
-## Reproducibility
-
-Every number in the paper is regenerable from this checkpoint. On identical hardware the short-document
-tasks reproduce **bit-for-bit** (Math and Twitter: identical top-100 documents, identical order, identical
-cosine scores); the long-document tasks reproduce to within 0.0006 NDCG@10 (Writing 0.00015, Congress
-0.00056), because sequences at the 1024-token cap accumulate more bf16 rounding across GPU generations.
-Expect third-decimal movement on Writing and Congress on different hardware.
+| Base | `nvidia/llama-nv-embed-reasoning-3b` (Llama-3.2-3B bi-encoder) |
+| Adapter (before merging) | LoRA r=16, α=32, dropout 0.05 on q/k/v/o/gate/up/down (~24M params) |
+| Pooling | mean over tokens, then L2 normalise → 3072-dim |
+| Loss | CachedMultipleNegativesRankingLoss, τ = 1/0.02 |
+| LR | 2e-4, 3% warmup, linear decay |
+| Batch | 4 per device × 40 GPUs × 2 grad-accum = 320 anchors |
+| Seq length | 1024 (queries truncated to 256 at evaluation) |
+| Schedule | 1 epoch, 40× H100, ~50 min |
+| Training rows | 154,361 (149,361 synthetic + 5,000 authorship kNN pairs) |
 
 ## Licence and intended use
 
-The adapter weights are released under **CC BY-NC 4.0**. Use also requires accepting the terms of the base
-model `nvidia/llama-nv-embed-reasoning-3b` and of Llama-3.2, which it derives from. Note that NVIDIA's
-repository is internally inconsistent about its licence (its `LICENSE` file states CC BY-NC 4.0 while its
-README front-matter states CC BY 4.0); check with NVIDIA before any commercial use.
+Released under **CC BY-NC 4.0**. This repository redistributes weights derived from
+`nvidia/llama-nv-embed-reasoning-3b`, which NVIDIA releases for **non-commercial / research use only**; their
+licence is included as `LICENSE_nvidia_base_model.txt` and their attribution notice as `NOTICE.txt`.
+
+**Built with Llama.** The base model derives from `meta-llama/Llama-3.2-3B` and the
+[Llama 3.2 Community License](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE.txt) applies
+in addition to the terms above.
 
-**Intended use** is research on latent-attribute retrieval. Please note that retrieval by authorial
-fingerprint can be used to link texts to an author across topics and venues, and therefore to deanonymise
-writers who rely on pseudonymity. The authorship encoder we distil is trained on Reddit data, and because
-style correlates with demography and dialect, the distilled neighbourhood structure may carry those
-correlates. We did not audit for this. Any deployment over people's writing should.
+**Intended use** is research on latent-attribute retrieval. Retrieval by authorial fingerprint can link texts
+to an author across topics and venues, and so deanonymise writers who rely on pseudonymity. The authorship
+encoder we distil is trained on Reddit data, and because style correlates with demography and dialect, the
+distilled neighbourhood structure may carry those correlates. We did not audit for this; any deployment over
+people's writing should.
 
 ## Citation
 

adapter_config.json

@@ -1,48 +0,0 @@
-{
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-  "alpha_pattern": {},
-  "arrow_config": null,
-  "auto_mapping": null,
-  "base_model_name_or_path": "nvidia/llama-nv-embed-reasoning-3b",
-  "bias": "none",
-  "corda_config": null,
-  "ensure_weight_tying": false,
-  "eva_config": null,
-  "exclude_modules": null,
-  "fan_in_fan_out": false,
-  "inference_mode": false,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 32,
-  "lora_bias": false,
-  "lora_dropout": 0.05,
-  "lora_ga_config": null,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "peft_version": "0.19.1",
-  "qalora_group_size": 16,
-  "r": 16,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "k_proj",
-    "o_proj",
-    "up_proj",
-    "gate_proj",
-    "down_proj",
-    "v_proj",
-    "q_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "FEATURE_EXTRACTION",
-  "trainable_token_indices": null,
-  "use_bdlora": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}

config.json

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+{
+  "architectures": [
+    "LlamaBidirectionalModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "llama_bidirectional_model.LlamaBidirectionalConfig",
+    "AutoModel": "llama_bidirectional_model.LlamaBidirectionalModel"
+  },
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama_bidirec",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pooling": "avg",
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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tokenizer.json

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tokenizer_config.json

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