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Model: Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1 Source: Original Platform
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267
README.md
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README.md
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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- mistralai/Mistral-7B-Instruct-v0.3
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- microsoft/Phi-3-mini-4k-instruct
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- microsoft/phi-2
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- HuggingFaceTB/SmolLM2-1.7B-Instruct
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- ibm-granite/granite-3.0-2b-instruct
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- EleutherAI/pythia-2.8b
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- EleutherAI/pythia-1.4b
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- facebook/opt-2.7b
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base_model_relation: merge
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- merge
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- model-merge
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- cross-architecture
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- cross-family
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- cross-family-merge
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- weight-merge
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- training-free
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- training-free-merge
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- procrustes
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- canonical-key-namespace
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- svd-filter
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- crdt-merge
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- qwen
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- qwen2
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- qwen2.5
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- instruction-tuned
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- reasoning
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- mathematical-reasoning
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- instruction-following
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- 7b
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- text-generation
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- llama-factory-compatible
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- vllm-compatible
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- llama-cpp-compatible
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model-index:
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- name: Qwen2.5-7B-Instruct-borg-merge-v1
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results:
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- task:
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type: text-generation
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name: Grade School Math
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dataset:
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name: GSM8K
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type: gsm8k
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split: test
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metrics:
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- type: exact_match
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value: 0.8446
|
||||
name: exact_match (strict-match)
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verified: false
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- task:
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type: text-generation
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name: AI2 Reasoning Challenge
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dataset:
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name: ARC-Challenge
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type: ai2_arc
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split: test
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metrics:
|
||||
- type: acc_norm
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||||
value: 0.5572
|
||||
name: acc_norm
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verified: false
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- task:
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type: text-generation
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name: Instruction Following
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dataset:
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name: IFEval
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type: ifeval
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split: test
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metrics:
|
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- type: inst_level_strict_acc
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value: 0.6811
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name: instruction-level strict accuracy
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verified: false
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- task:
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type: text-generation
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name: Massive Multitask Language Understanding
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dataset:
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name: MMLU
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type: cais/mmlu
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split: test
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metrics:
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||||
- type: acc
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value: 0.7094
|
||||
name: acc
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||||
verified: false
|
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- task:
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type: text-generation
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name: TruthfulQA
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dataset:
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name: TruthfulQA mc2
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type: truthful_qa
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split: validation
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metrics:
|
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- type: acc
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||||
value: 0.6285
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name: mc2
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verified: false
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- task:
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type: text-generation
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name: Commonsense Reasoning
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dataset:
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name: HellaSwag
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type: hellaswag
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split: validation
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metrics:
|
||||
- type: acc
|
||||
value: 0.6830
|
||||
name: acc
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||||
verified: false
|
||||
- task:
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type: text-generation
|
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name: Physical Commonsense
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dataset:
|
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name: PIQA
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type: ybisk/piqa
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split: validation
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metrics:
|
||||
- type: acc
|
||||
value: 0.8014
|
||||
name: acc
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||||
verified: false
|
||||
- task:
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type: text-generation
|
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name: Code Generation
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dataset:
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name: HumanEval
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type: openai_humaneval
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split: test
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metrics:
|
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- type: pass@1
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value: 0.5854
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name: pass@1 (greedy)
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verified: false
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---
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# Qwen2.5-7B-Instruct-borg-merge-v1
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**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`.**
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| Task | Anchor SOLO | This model | Δ |
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|---|---:|---:|---:|
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||||
| **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 |
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| 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 |
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||||
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||||
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.
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
|
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"Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("Optitransfer/Qwen2.5-7B-Instruct-borg-merge-v1")
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prompt = "Q: What is 17 multiplied by 23? Show your work.\nA:"
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ids = tokenizer(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**ids, max_new_tokens=128, do_sample=False)
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print(tokenizer.decode(out[0], skip_special_tokens=True))
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```
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Compatible with `vLLM`, `llama.cpp` (after GGUF conversion), `text-generation-inference`, `text-generation-webui`, and any standard HuggingFace inference stack.
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## What's special about this merge
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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.
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This model is the result of a training-free pipeline that solves this:
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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`.
|
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5. **Decanonicalize** to the anchor's native key namespace; save as standard `safetensors`.
|
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|
||||
This is the **asymmetric tolerance recipe**: tight on attention to preserve circuits, loose on FFN to absorb knowledge.
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||||
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## 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.
|
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## 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.
|
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|
||||
## 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).
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||||
- **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.
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## Intended use
|
||||
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- Research and evaluation of cross-family weight-merging techniques.
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||||
- 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.
|
||||
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||||
## 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"*.
|
||||
|
||||
|
||||
24
added_tokens.json
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
|
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|
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|
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"<|endoftext|>": 151643,
|
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"<|file_sep|>": 151664,
|
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|
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|
||||
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|
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|
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"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
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}
|
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54
chat_template.jinja
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chat_template.jinja
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||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{%- endif %}
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58
config.json
Normal file
58
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Normal file
@@ -0,0 +1,58 @@
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generation_config.json
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14
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Normal file
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|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user