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Model: iic/ERank-4B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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---
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<div align="center">
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<h1>ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking</h1>
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</div>
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<p align="center">
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<a href="https://arxiv.org/abs/2509.00520">Arxiv</a>
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</p>
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## Introduction
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We introduce ERANK, a highly effective and efficient pointwise reranker built from a reasoning LLM, which excels across diverse relevance scenarios with low latency.
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Surprisingly, it also outperforms recent listwise rerankers on the most challenging reasoning-intensive tasks.
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<img src="./assets/overview.png">
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ERank is trained with a novel two-stage training pipeline, i.e., Supervised Fine-Tuning (SFT) and Reinforcement
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Learning (RL).
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During the SFT stage, unlike traidtional pointwise rerankers that train the LLMs for binary relevance classification, we encourage the LLM to generatively output fine grained integer scores.
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In the RL training, we introduce a novel listwise derived reward, which instills global ranking awareness into the efficient
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pointwise architecture.
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## Model List
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We provide the trained reranking models in various sizes (4B, 14B and 32B), all of which support customizing the input instruction according to different tasks.
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| Model | Size | Layers | Sequence Length | Instruction Aware |
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|------------------------------------------|------|--------|-----------------|-------------------|
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| [ERank-4B](https://huggingface.co/Alibaba-NLP/ERank-4B) | 4B | 36 | 32K | Yes |
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| [ERank-14B](https://huggingface.co/Alibaba-NLP/ERank-14B) | 14B | 40 | 128K | Yes |
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| [ERank-32B](https://huggingface.co/Alibaba-NLP/ERank-32B) | 32B | 64 | 128K | Yes |
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## Evaluation
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We evaluate ERank on both reasoning-intensive benchmarks (BRIGHT and FollowIR) and traditional semantic relevance benchmarks (BEIR and TREC DL).
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All methods use the original queries without hybrid scores.
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| Paradigm | Method | Average | BRIGHT | FollowIR | BEIR | TREC DL |
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| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
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| - | First-stage retriever | 25.9 | 13.7 | 0 | 40.8 | 49.3 |
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| Listwise | Rank-R1-7B | 34.6 | 15.7 | 3.6 | **49.0** | 70.0 |
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| Listwise | Rearank-7B | 35.3 | 17.4 | 2.3 | **49.0** | **72.5** |
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| Pointwise | JudgeRank-8B | 32.1 | 17.0 | 9.9 | 39.1 | 62.6 |
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| Pointwise | Rank1-7B | 34.6 | 18.2 | 9.1 | 44.2 | 67.1 |
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| Pointwise | **ERank-4B (Ours)** | 36.8 | 22.7 | 11.0 | 44.8 | 68.9 |
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| Pointwise | **ERank-14B (Ours)** | 36.9 | 23.1 | 10.3 | 47.1 | 67.1 |
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| Pointwise | **ERank-32B (Ours)** | **38.1** | **24.4** | **12.1** | 47.7 | 68.1 |
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On the most challenging BRIGHT benchmark, with top-100 documents retrieved by ReasonIR-8B using GPT-4 reason-query, ERank with BM25 hybrid achieves the state-of-the-art NDCG@10.
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| Method | nDCG@10 |
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| :--- | :--- |
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| ReasonIR-8B | 30.5 |
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| Rank-R1-7B | 24.1 |
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| Rank1-7B | 24.3 |
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| Rearank-7B | 27.5 |
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| JudgeRank-8B | 20.2 |
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| *+ BM25 hybrid* | 22.7 |
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| Rank-R1-32B-v0.2 | 37.7 |
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| *+ BM25 hybrid* | 40.0 |
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| **ERank-4B (Ours)** | 30.5 |
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| *+ BM25 hybrid* | 38.7 |
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| **ERank-14B (Ours)** | 31.8 |
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| *+ BM25 hybrid* | 39.3 |
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| **ERank-32B (Ours)** | 32.8 |
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| *+ BM25 hybrid* | **40.2** |
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Since ERank is a pointwise reranker, it has low latency compared with listwise models.
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<div align="center">
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<img src="./assets/latency.png" width=400px>
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</div>
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For more details, please refer to our [Paper](https://arxiv.org/abs/2509.00520).
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## Usage
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We have implemented the inference code based on Transformer and vLLM, respectively.
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```python
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from examples.ERank_Transformer import ERank_Transformer
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from examples.ERank_vLLM import ERank_vLLM
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from examples.utils import hybrid_scores
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# select a model
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model_name_or_path = "Alibaba-NLP/ERank-4B"
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# model_name_or_path = "Alibaba-NLP/ERank-14B"
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# model_name_or_path = "Alibaba-NLP/ERank-32B"
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# use vLLM or Transformer
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# reranker = ERank_Transformer(model_name_or_path)
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reranker = ERank_vLLM(model_name_or_path)
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# input data
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instruction = "Retrieve relevant documents for the query."
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query = "I am happy"
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docs = [
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{"content": "excited", "first_stage_score": 46.7},
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{"content": "sad", "first_stage_score": 1.5},
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{"content": "peaceful", "first_stage_score": 2.3},
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]
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# rerank
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results = reranker.rerank(query, docs, instruction, truncate_length=2048)
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print(results)
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# [
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# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84},
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# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98}
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# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0},
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# ]
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# Optional: hybrid with first-stage scores
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alpha = 0.2
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hybrid_results = hybrid_scores(results, alpha)
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print(hybrid_results)
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# [
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# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84, 'hybrid_score': 1.18},
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# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98, 'hybrid_score':0.01},
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# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0, 'hybrid_score': -1.19}
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# ]
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```
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Please refer to the `examples` directory for details, in which we also provide the instructions used in the prompt during evaluation.
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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@misc{ERank,
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title={ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking},
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author={Yuzheng Cai and Yanzhao Zhang and Dingkun Long and Mingxin Li and Pengjun Xie and Weiguo Zheng},
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year={2025},
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eprint={2509.00520},
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archivePrefix={arXiv},
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primaryClass={cs.IR},
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url={https://arxiv.org/abs/2509.00520},
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}
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```
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added_tokens.json
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BIN
assets/latency.png
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assets/overview.png
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"eos_token_id": 151643,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"max_position_embeddings": 32768,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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configuration.json
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configuration.json
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{"framework":"Pytorch","task":"text-generation"}
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examples/ERank_Transformer.py
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examples/ERank_Transformer.py
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from torch.nn import functional as F
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from utils import prompt_template, truncate, hybrid_scores
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class ERank_Transformer:
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def __init__(self, model_name_or_path: str):
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"""
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Initializes the ERank_Transformer reranker.
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Args:
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model_name_or_path (str): The name or path of the model to be loaded.
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This can be a Hugging Face model ID or a local path.
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"""
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self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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self.reranker = AutoModelForCausalLM.from_pretrained(model_name_or_path).eval()
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self.reranker.to("cuda")
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def rerank(self, query: str, docs: list, instruction: str, truncate_length: int=None) -> list:
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"""
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Reranks a list of documents based on a query and a specific instruction.
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Args:
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query (str): The search query provided by the user.
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docs (list): A list of dictionaries, where each dictionary represents a document
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and must contain a "content" key.
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instruction (str): The instruction for the model, guiding it on how to evaluate the documents.
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truncate_length (int, optional): The maximum length to truncate the query and document content to. Defaults to None.
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Returns:
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list: A new list of document dictionaries, sorted by their "rank_score" in descending order.
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"""
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# prepare messages
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messages = [
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[{
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"role": "user",
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"content": prompt_template.format(
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query=truncate(self.tokenizer, query, length=truncate_length) if truncate_length else query,
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doc=truncate(self.tokenizer, doc["content"], length=truncate_length) if truncate_length else doc["content"],
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instruction=instruction
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)
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}] for doc in docs
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]
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# encode tokens
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texts = [
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self.tokenizer.apply_chat_template(
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each,
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tokenize=False,
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add_generation_prompt=True,
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) for each in messages
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]
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inputs = self.tokenizer(texts, padding=True, return_tensors="pt").to(self.reranker.device)
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# LLM completion
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outputs = self.reranker.generate(
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**inputs,
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max_new_tokens=8192,
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output_scores=True,
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return_dict_in_generate=True
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)
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# extract and organize results
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results = []
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scores = outputs.scores
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generated_ids = outputs.sequences
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answer_token_ids = self.tokenizer.encode("<answer>", add_special_tokens=False)
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for idx in range(len(texts)):
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# find <answer> in the generated sequence
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output_ids = generated_ids[idx].tolist()
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start_index = -1
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for i in range(len(output_ids)-len(answer_token_ids)-1, -1, -1):
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if output_ids[i:i + len(answer_token_ids)] == answer_token_ids:
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start_index = i + len(answer_token_ids)
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break
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# start from the index after <answer>
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answer = ""
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prob = 1.0
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if start_index != -1:
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for t in range(start_index - inputs.input_ids.size(1), len(scores)):
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generated_token_id = generated_ids[idx][inputs.input_ids.size(1) + t]
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token = self.tokenizer.decode(generated_token_id)
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if token.isdigit():
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logits = scores[t][idx]
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probs = F.softmax(logits, dim=-1)
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prob *= probs[generated_token_id].item()
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answer += token
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else:
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break
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# in case the answer is not a digit or exceeds 10
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try:
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answer = int(answer)
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assert answer <= 10
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except:
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answer = -1
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# append to the final results
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results.append({
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**docs[idx],
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"rank_score": answer * prob
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})
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# sort the reranking results for the query
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results.sort(key=lambda x:x["rank_score"], reverse=True)
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return results
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if __name__ == "__main__":
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# select a model
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model_name_or_path = "Ucreate/ERank-4B"
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# model_name_or_path = "Ucreate/ERank-14B"
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# model_name_or_path = "Ucreate/ERank-32B"
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reranker = ERank_Transformer(model_name_or_path)
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# input data
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instruction = "Retrieve relevant documents for the query."
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query = "I am happy"
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docs = [
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{"content": "excited", "first_stage_score": 46.7},
|
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{"content": "sad", "first_stage_score": 1.5},
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{"content": "peaceful", "first_stage_score": 2.3},
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]
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# rerank
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results = reranker.rerank(query, docs, instruction, truncate_length=2048)
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print(results)
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# [
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# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84},
|
||||
# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98}
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# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0},
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# ]
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# Optional: hybrid with first-stage scores
|
||||
alpha = 0.2
|
||||
hybrid_results = hybrid_scores(results, alpha)
|
||||
print(hybrid_results)
|
||||
# [
|
||||
# {'content': 'excited', 'first_stage_score': 46.7, 'rank_score': 4.84, 'hybrid_score': 1.18},
|
||||
# {'content': 'peaceful', 'first_stage_score': 2.3, 'rank_score': 2.98, 'hybrid_score':0.01},
|
||||
# {'content': 'sad', 'first_stage_score': 1.5, 'rank_score': 0.0, 'hybrid_score': -1.19}
|
||||
# ]
|
||||
97
examples/ERank_vLLM.py
Normal file
97
examples/ERank_vLLM.py
Normal file
@@ -0,0 +1,97 @@
|
||||
import torch
|
||||
import math
|
||||
from vllm import LLM, SamplingParams
|
||||
from utils import prompt_template, truncate
|
||||
|
||||
|
||||
class ERank_vLLM:
|
||||
|
||||
def __init__(self, model_name_or_path: str):
|
||||
"""
|
||||
Initializes the ERank_vLLM reranker.
|
||||
|
||||
Args:
|
||||
model_name_or_path (str): The name or path of the model to be loaded.
|
||||
This can be a Hugging Face model ID or a local path.
|
||||
"""
|
||||
num_gpu = torch.cuda.device_count()
|
||||
self.ranker = LLM(
|
||||
model=model_name_or_path,
|
||||
tensor_parallel_size=num_gpu,
|
||||
gpu_memory_utilization=0.95,
|
||||
enable_prefix_caching=True
|
||||
)
|
||||
self.tokenizer = self.ranker.get_tokenizer()
|
||||
self.sampling_params = SamplingParams(
|
||||
temperature=0,
|
||||
max_tokens=4096,
|
||||
logprobs=20
|
||||
)
|
||||
|
||||
def rerank(self, query: str, docs: list, instruction: str, truncate_length: int=None) -> list:
|
||||
"""
|
||||
Reranks a list of documents based on a query and a specific instruction.
|
||||
|
||||
Args:
|
||||
query (str): The search query provided by the user.
|
||||
docs (list): A list of dictionaries, where each dictionary represents a document
|
||||
and must contain a "content" key.
|
||||
instruction (str): The instruction for the model, guiding it on how to evaluate the documents.
|
||||
truncate_length (int, optional): The maximum length to truncate the query and document content to. Defaults to None.
|
||||
|
||||
Returns:
|
||||
list: A new list of document dictionaries, sorted by their "rank_score" in descending order.
|
||||
"""
|
||||
|
||||
# prepare messages
|
||||
messages = [
|
||||
[{
|
||||
"role": "user",
|
||||
"content": prompt_template.format(
|
||||
query=truncate(self.tokenizer, query, length=truncate_length) if truncate_length else query,
|
||||
doc=truncate(self.tokenizer, doc["content"], length=truncate_length) if truncate_length else doc["content"],
|
||||
instruction=instruction
|
||||
)
|
||||
}] for doc in docs
|
||||
]
|
||||
|
||||
# LLM generate
|
||||
outputs = self.ranker.chat(messages, self.sampling_params)
|
||||
|
||||
# extract and organize results
|
||||
results = []
|
||||
for doc, output in zip(docs, outputs):
|
||||
|
||||
# extract the answer and its probability
|
||||
cur = ""
|
||||
answer = ""
|
||||
is_ans = False
|
||||
prob = 1.0
|
||||
for each in output.outputs[0].logprobs[-10:]:
|
||||
_, detail = next(iter(each.items()))
|
||||
token = detail.decoded_token
|
||||
logprob = detail.logprob
|
||||
if is_ans and token.isdigit():
|
||||
answer += token
|
||||
prob *= math.exp(logprob)
|
||||
else:
|
||||
cur += token
|
||||
if cur.endswith("<answer>"):
|
||||
is_ans = True
|
||||
|
||||
# in case the answer is not a digit or exceeds 10
|
||||
try:
|
||||
answer = int(answer)
|
||||
assert answer <= 10
|
||||
except:
|
||||
answer = -1
|
||||
|
||||
# append to the final results
|
||||
results.append({
|
||||
**doc,
|
||||
"rank_score": answer * prob
|
||||
})
|
||||
|
||||
# sort the reranking results for the query
|
||||
results.sort(key=lambda x:x["rank_score"], reverse=True)
|
||||
return results
|
||||
10
examples/instructions.json
Normal file
10
examples/instructions.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"BRIGHT (AoPS)": "We want to find different but similar math problems to the query. A document is relevant if it uses the same class of functions and shares any overlapping techniques.",
|
||||
"BRIGHT (LeetCode)": "I am looking to find different problems that share similar data structures (of any kind) or algorithms (e.g. DFS, DP, sorting, traversals, etc.). I am looking for problems that share one or both of these similarities to the query. Does the passage below share any similarities? e.g. if there was a textbook on leetcode problems, this would be in the same book even though it could be in a different chapter.",
|
||||
"BRIGHT (Pony)": "I will use the programming language pony. But to solve the problem above, I need to know things about pony. A passage is relevant if it contains docs that match any part (even basic parts) of the code I will have to write for the above program.",
|
||||
"BRIGHT (TheoremQA-Q)": "We want to find a document which uses the same mathematical process as the query. A document is relevant if it uses the same mathematical process as the query.",
|
||||
"BRIGHT (TheoremQA-T)": "We want to find a document which uses the same mathematical process as the query. A document is relevant if it uses the same mathematical process as the query.",
|
||||
"BRIGHT (others)": "A document is relevant if it contains information that helps answer or address the query. A document is not relevant if it doesn't contain information that helps answer the query, even if it mentions similar topics.",
|
||||
"BEIR / TREC DL": "Given a query, retrieval relevant passage.",
|
||||
"FollowIR": "Retrieval the relevant passage for the given query. Be careful about the extra requirements about relevance in the query."
|
||||
}
|
||||
44
examples/utils.py
Normal file
44
examples/utils.py
Normal file
@@ -0,0 +1,44 @@
|
||||
import numpy as np
|
||||
|
||||
prompt_template = """Given a query and a document, please give a relevance score of 0~10.
|
||||
The goal or relevance definition is: {instruction}
|
||||
|
||||
Here is the query:
|
||||
{query}
|
||||
|
||||
Here is the document:
|
||||
{doc}
|
||||
|
||||
After thinking, directly choose a relevance score from [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10].
|
||||
- 0 represents completely not related
|
||||
- 10 means perfectly related.
|
||||
|
||||
Desired output format:
|
||||
<think>put your thinking here</think><answer>Only allows an integer here</answer>
|
||||
|
||||
Your output:"""
|
||||
|
||||
|
||||
def truncate(tokenizer, text, length):
|
||||
if length == None or text == None:
|
||||
return text
|
||||
return tokenizer.convert_tokens_to_string(tokenizer.tokenize(text)[:length])
|
||||
|
||||
|
||||
def hybrid_scores(results, alpha):
|
||||
first_stage_scores = [each["first_stage_score"] for each in results]
|
||||
rank_scores = [each["rank_score"] for each in results]
|
||||
first_stage_mean, first_stage_std = np.mean(first_stage_scores), np.std(first_stage_scores)
|
||||
rank_mean, rank_std = np.mean(rank_scores), np.std(rank_scores)
|
||||
|
||||
hybrid_results = []
|
||||
for result in results:
|
||||
normalized_first_stage_score = (result["first_stage_score"] - first_stage_mean) / first_stage_std
|
||||
normalized_rank_score = (result["rank_score"] - rank_mean) / rank_std
|
||||
hybrid_results.append({
|
||||
**result,
|
||||
"hybrid_score": float(alpha * normalized_first_stage_score + (1-alpha) * normalized_rank_score)
|
||||
})
|
||||
hybrid_results.sort(key=lambda x:x['hybrid_score'], reverse=True)
|
||||
|
||||
return hybrid_results
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"max_new_tokens": 2048,
|
||||
"transformers_version": "4.51.3"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8f50ba1f8e5b936d5ea782466732ad5226b86d578a6461d76eedb8a85c3cb84f
|
||||
size 4697225688
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0c8a5177b158bdb29f7d6cbe2f835a05bd203af9010dbe847216d8cd17f78b86
|
||||
size 4125668816
|
||||
406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 8822848512
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|
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||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
757480
tokenizer.json
Normal file
757480
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
241
tokenizer_config.json
Normal file
241
tokenizer_config.json
Normal file
@@ -0,0 +1,241 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ 'System: ' + system_message + '<|endoftext|>' + '\n' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '<|endoftext|>' + '\nAssistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + '\n' }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"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