--- license: apache-2.0 base_model: - Qwen/Qwen2.5-7B-Instruct - mistralai/Mistral-7B-Instruct-v0.3 - microsoft/Phi-3-mini-4k-instruct - microsoft/phi-2 - HuggingFaceTB/SmolLM2-1.7B-Instruct - ibm-granite/granite-3.0-2b-instruct - EleutherAI/pythia-2.8b - EleutherAI/pythia-1.4b - facebook/opt-2.7b base_model_relation: merge language: - en library_name: transformers pipeline_tag: text-generation tags: - merge - model-merge - cross-architecture - cross-family - cross-family-merge - weight-merge - training-free - training-free-merge - procrustes - canonical-key-namespace - svd-filter - crdt-merge - qwen - qwen2 - qwen2.5 - instruction-tuned - reasoning - mathematical-reasoning - instruction-following - 7b - text-generation - llama-factory-compatible - vllm-compatible - llama-cpp-compatible model-index: - name: Qwen2.5-7B-Instruct-borg-merge-v1 results: - task: type: text-generation name: Grade School Math dataset: name: GSM8K type: gsm8k split: test metrics: - type: exact_match value: 0.8446 name: exact_match (strict-match) verified: false - task: type: text-generation name: AI2 Reasoning Challenge dataset: name: ARC-Challenge type: ai2_arc split: test metrics: - type: acc_norm value: 0.5572 name: acc_norm verified: false - task: type: text-generation name: Instruction Following dataset: name: IFEval type: ifeval split: test metrics: - type: inst_level_strict_acc value: 0.6811 name: instruction-level strict accuracy verified: false - task: type: text-generation name: Massive Multitask Language Understanding dataset: name: MMLU type: cais/mmlu split: test metrics: - type: acc value: 0.7094 name: acc verified: false - task: type: text-generation name: TruthfulQA dataset: name: TruthfulQA mc2 type: truthful_qa split: validation metrics: - type: acc value: 0.6285 name: mc2 verified: false - task: type: text-generation name: Commonsense Reasoning dataset: name: HellaSwag type: hellaswag split: validation metrics: - type: acc value: 0.6830 name: acc verified: false - task: type: text-generation name: Physical Commonsense dataset: name: PIQA type: ybisk/piqa split: validation metrics: - type: acc value: 0.8014 name: acc verified: false - task: type: text-generation name: Code Generation dataset: name: HumanEval type: openai_humaneval split: test metrics: - type: pass@1 value: 0.5854 name: pass@1 (greedy) verified: false --- # Qwen2.5-7B-Instruct-borg-merge-v1 **A training-free cross-family weight merge of Qwen2.5-7B-Instruct with 8 donors from 4 architecture families. Lifts GSM8K +3.3 pp, ARC-Challenge +3.2 pp, and IFEval +2.6 pp absolute over the unmerged anchor. No fine-tuning. No distillation. No router. Drop-in `safetensors`.** | Task | Anchor SOLO | This model | Δ | |---|---:|---:|---:| | **GSM8K** (`exact_match,strict-match`) | 0.8120 | **0.8446** | **+0.0326** | | **ARC-Challenge** (`acc_norm,none`) | 0.5256 | **0.5572** | **+0.0316** | | **IFEval** (`inst_level_strict_acc,none`) | 0.6547 | **0.6811** | **+0.0264** | | MMLU (`acc,none`) | 0.7180 | 0.7094 | -0.0086 | | TruthfulQA mc2 (`acc,none`) | 0.6475 | 0.6285 | -0.0190 | | HellaSwag (`acc,none`) | 0.6895 | 0.6830 | -0.0065 | | PIQA (`acc,none`) | 0.8030 | 0.8014 | -0.0016 | | HumanEval (`pass@1,greedy`) | 0.6463 | 0.5854 | -0.0610 | Lifts on **3 of 8 standard benchmarks** vs. the unmerged anchor -- on the tasks where the donor pool is competence-concentrated (instruction following + broad reasoning). Regresses on HumanEval, where the donor pool was code-light by design. The regression structure is itself a falsifiable prediction about the recipe. ## Quick start ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( "Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1", torch_dtype=torch.float16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1") prompt = "Q: What is 17 multiplied by 23? Show your work.\nA:" ids = tokenizer(prompt, return_tensors="pt").to(model.device) out = model.generate(**ids, max_new_tokens=128, do_sample=False) print(tokenizer.decode(out[0], skip_special_tokens=True)) ``` Compatible with `vLLM`, `llama.cpp` (after GGUF conversion), `text-generation-inference`, `text-generation-webui`, and any standard HuggingFace inference stack. ## What's special about this merge Cross-family weight merging across architecture families (Llama, Phi, NeoX, OPT) is conventionally considered impossible -- different attention head dimensions, different FFN expansion factors, different vocabularies. A naive linear interpolation between, say, a Qwen attention block and a Mistral attention block does not even type-check. This model is the result of a training-free pipeline that solves this: 1. **Canonicalize** each donor's tensors into a shared key namespace via per-architecture detectors (10 architecture families covered: BERT, RoBERTa, Llama/Qwen, Mistral, Pythia, OPT, Phi, T5, w2v-bert, and more). 2. **Procrustes-align** each donor's basis to the anchor via per-tensor orthogonal rotation (smaller-side SVD). 3. **Compute donor deltas** in canonical space; filter via per-role tolerance (asymmetric: `τ_attn=0.05`, `τ_ffn=0.20`); keep top-3 SVD components. 4. **Absorb** the rotated, filtered, low-rank delta into the anchor with anchor blend `β=0.60`. 5. **Decanonicalize** to the anchor's native key namespace; save as standard `safetensors`. This is the **asymmetric tolerance recipe**: tight on attention to preserve circuits, loose on FFN to absorb knowledge. ## Donor pool (8 donors, 4 architecture families) | Source | Family | License | |---|---|---| | Qwen/Qwen2.5-7B-Instruct (anchor) | Qwen / Llama-arch | Apache 2.0 | | mistralai/Mistral-7B-Instruct-v0.3 | Mistral / Llama-arch | Apache 2.0 | | microsoft/Phi-3-mini-4k-instruct | Phi (new) | MIT | | microsoft/phi-2 | Phi (old) | MIT | | HuggingFaceTB/SmolLM2-1.7B-Instruct | Llama-arch (small) | Apache 2.0 | | ibm-granite/granite-3.0-2b-instruct | Llama-arch (Granite tweaks) | Apache 2.0 | | EleutherAI/pythia-2.8b | NeoX | Apache 2.0 | | EleutherAI/pythia-1.4b | NeoX | Apache 2.0 | | facebook/opt-2.7b | OPT | OPT license | ## Verification - **Cross-run reproducibility**: an independent preflight evaluation two days prior to the headline run produces byte-identical scores to all 16 decimal places across every overlapping (variant, task) cell. The merge is fully deterministic. - **Pre-flight gates**: G1 round-trip across all 6 cross-family canonicalization tests reports `r_max=0.0`, `n_bad=0` (lossless canonical key namespace). G3 multi-seed slice-bias on the anchor MMLU 200-sample slice returns `0.7480126320374605` to 16 decimal places across seeds 7, 42, 1337. G4 anchor MMLU full matches the published Qwen2.5-7B-Instruct leaderboard reference. - **Behavioural inspection**: 5 reasoning-heavy prompts (math word problem, French translation, long-multiplication, recursive Fibonacci, factual enumeration) produce coherent, instruction-following, mathematically-correct output with no gibberish, no tokenizer drift, no instruction-format collapse. - **Eval framework**: `lm-eval-harness` 0.4.4 with `transformers` 4.55.0, `tokenizers` 0.21.4, `datasets` >=2.20 <4.0, fp16, batch 2, single A100 80GB. ## Comparison to recent work in the model-merging landscape For a comprehensive map of model-merging methods, theory, and applications, see Yang et al.'s curated survey **Awesome-Model-Merging-Methods-Theories-Applications** (forthcoming *ACM Computing Surveys 2026*). Closest direct relatives: - **Transport and Merge** (Cui et al., Feb 2026) -- cross-architecture merging via activation-space optimal transport. Different problem class: theirs produces a runtime-aligned composition; this model is a permanent merged checkpoint. - **Unconstrained Model Merging for Enhanced LLM Reasoning** (Zhang et al., Oct 2024) -- closest direct relative on substrate scale (7B-class) and donor count (9 reasoning-optimized LLMs). The result above extends this lineage with absolute benchmark deltas against a state-competitive instruction-tuned anchor. - **Git Re-Basin** (Ainsworth, Hayase & Srinivasa, ICLR 2023) -- same-architecture merging modulo permutation symmetries. The pipeline above is essentially the cross-architecture generalization (continuous Procrustes rotation rather than discrete permutation matching). - **OT-Fusion** (Singh & Jaggi, NeurIPS 2020) -- same-architecture optimal transport on weight rows. Spiritual ancestor of Cui et al.'s 2026 cross-architecture extension. - **REPAIR** (Jordan et al., 2022) -- re-normalization to address variance collapse after permutation interpolation. The pipeline above sidesteps this by using anchor-plus-delta absorption rather than midpoint interpolation. ## Limitations - **Code generation regresses** by 6.10 pp on HumanEval. The donor pool was reasoning-heavy and instruction-tuned; it contained no code-specialist models (CodeLlama, StarCoder, Qwen2.5-Coder). Documented as falsifiable prediction: a code-heavy donor pool should restore HumanEval while preserving the GSM8K, ARC-Challenge, and IFEval gains. This is the explicit subject of the next research cycle. - **Mild MMLU regression** (-0.86 pp). The merge trades some broad knowledge for instruction-following + reasoning concentration. Within typical eval noise on TruthfulQA mc2 (-0.19), HellaSwag (-0.07), PIQA (-0.02). - **Single substrate tested**: results are on Qwen2.5-7B-Instruct. Generalization to other instruction-tuned 7B-class anchors (Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3 as anchor, etc.) is the next experiment. - **HumanEval pass@1 measured via custom isolated-subprocess scorer**, not via lm-eval (the pinned `lm-eval-harness 0.4.4` does not ship the humaneval task). Greedy decoding, 164 problems, no temperature sweep. Identical methodology to bigcode-evaluation-harness with subprocess-isolated test execution. ## Intended use - Research and evaluation of cross-family weight-merging techniques. - Drop-in replacement for `Qwen/Qwen2.5-7B-Instruct` in workflows where the trade-off (GSM8K / ARC-Challenge / IFEval lifts vs. mild HumanEval regression) is favorable. - Compatible with vLLM, llama.cpp (after GGUF conversion), TGI, text-generation-webui, and any standard HuggingFace inference stack. ## Out of scope - Code generation as primary use case -- use `Qwen/Qwen2.5-Coder-7B-Instruct` instead, or wait for the next merge variant which targets a code-heavy donor pool. - Production deployment without your own evaluation on your specific task distribution. ## Citation If you use this model, please cite: ```bibtex @misc{borg-merge-v1-2026, title = {Conflict-Free Replicated Datatypes for Neural Network Model Merging}, author = {Optitransfer}, year = {2026}, url = {https://huggingface.co/Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1} } ``` ## Contact - `rgillespie83@icloud.com` - `data@optitransfer.ch` For arXiv endorsement requests on the full technical paper covering cross-family weight merging (cs.LG / secondary cs.CL): same contacts, subject line *"arXiv endorsement: cross-family weight merging"*.