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Model: zyznull/RankingGPT-llama2-7b
Source: Original Platform

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README.md

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+---
+license: mit
+---
+
+# RankingGPT-llama2-7b
+
+RankingGPT is a text ranker based on large language models with significant in-domain and out-domain effectiveness.
+We provide RankingGPT in different sizes and types, including bloom-560m, bloom-1b1, bloom-3b, bloom-7b, llama2-7b, baichuan2-7b and qwen-7b.
+
+More details please refer to our [paper](https://arxiv.org/abs/2311.16720) and [github](https://github.com/Alibaba-NLP/RankingGPT).
+
+
+## Usage
+
+Code example
+```python
+import torch
+from transformers import AutoModelForCausalLM, LlamaTokenizer
+
+tokenizer = LlamaTokenizer.from_pretrained('zyznull/RankingGPT-llama2-7b',trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('zyznull/RankingGPT-llama2-7b',trust_remote_code=True).eval()
+
+query='when should a baby walk'
+document='Most babies start to walk around 13 months, but your baby may start walking as early as 9 or 10 months or as late as 15 or 16 months.'
+
+context=f'Document: {document} Query:'
+example=context+query
+
+context_enc = tokenizer.encode(context, add_special_tokens=False)
+continuation_enc = tokenizer.encode(query, add_special_tokens=False)
+model_input = torch.tensor(context_enc+continuation_enc[:-1])
+continuation_len = len(continuation_enc)
+input_len, = model_input.shape
+
+
+with torch.no_grad():
+    logprobs = torch.nn.functional.log_softmax(model(model_input.unsqueeze(dim=0))[0], dim=-1)[0]
+
+logprobs = logprobs[input_len-continuation_len:]
+logprobs = torch.gather(logprobs, 1, torch.tensor(continuation_enc).unsqueeze(-1)).squeeze(-1)
+score = torch.sum(logprobs)/logprobs.shape[0]
+
+print(f"Document: {document[:20] + '...'} Score: {score}")
+```
+
+### Result
+|         | DL19 | DL20 | BEIR | url |
+|---------|------|------|------|-----------------|
+| MonoBERT-340M | 72.3 | 70.3 | 50.5 |     [huggingface](https://huggingface.co/veneres/monobert-msmarco)          |
+| MonoT5-220M  | 71.5 | 69.7 | 49.3 |     [huggingface](https://huggingface.co/castorini/monot5-base-msmarco)          |
+| MonoT5-770M  | 73.2 | 71.2 | 53.1 |    [huggingface](https://huggingface.co/castorini/monot5-large-msmarco)          |
+| MonoT5-3B  | 72.8 | 74.5 | 54.6 |     [huggingface](https://huggingface.co/castorini/monot5-3b-msmarco)          |
+| RankT5-770M  | -    | -    | 53.7 |     [huggingface](https://huggingface.co/bergum/rank-T5-flan)           |
+| RankLLaMA| 74.6 | 76.6 | 52.5 |  [huggingface](https://huggingface.co/castorini/rankllama-v1-7b-lora-passage) |
+| RankingGPT-bloom-560m| 75.3 | 73.2 | 53.7 | [huggingface](https://huggingface.co/zyznull/RankingGPT-bloom-560m) [modelscope](https://modelscope.cn/models/damo/RankingGPT-bloom-560m)       |
+| RankingGPT-bloom-1b1| 75.6 | 73.2 | 54.5 | [huggingface](https://huggingface.co/zyznull/RankingGPT-bloom-1b1)  [modelscope](https://modelscope.cn/models/damo/RankingGPT-bloom-1b1)        |
+| RankingGPT-bloom-3b| 76.8 | 73.6 | 56.2 | [huggingface](https://huggingface.co/zyznull/RankingGPT-bloom-3b)  [modelscope](https://modelscope.cn/models/damo/RankingGPT-bloom-3b)        |
+| RankingGPT-bloom-7b| 77.3 | 74.6 | 56.6 | [huggingface](https://huggingface.co/zyznull/RankingGPT-bloom-7b)  [modelscope](https://modelscope.cn/models/damo/RankingGPT-bloom-7b)        |
+| RankingGPT-llama2-7b| 76.2 | 76.3 | 57.8 | [huggingface](https://huggingface.co/zyznull/RankingGPT-llama2-7b)  [modelscope](https://modelscope.cn/models/damo/RankingGPT-llama2-7b)        |
+| RankingGPT-baichuan2-7b| 75.9 | 74.3 | 57.5 |  [huggingface](https://huggingface.co/zyznull/RankingGPT-baichuan2-7b) [modelscope](https://modelscope.cn/models/damo/RankingGPT-baichuan2-7b)        |
+| RankingGPT-qwen-7b| 75.8 | 74.3 | 58.3 | [huggingface](https://huggingface.co/zyznull/RankingGPT-qwen-7b)  [modelscope](https://modelscope.cn/models/damo/RankingGPT-qwen-7b)        |
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{zhang2023rankinggpt,
+      title={RankingGPT: Empowering Large Language Models in Text Ranking with Progressive Enhancement}, 
+      author={Longhui Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang and Min Zhang},
+      year={2023},
+      eprint={2311.16720},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+```

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-2-7b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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+{
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+  "eos_token_id": 2,
+  "pad_token_id": 32000,
+  "temperature": 0.9,
+  "top_p": 0.6,
+  "transformers_version": "4.28.1"
+}

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special_tokens_map.json

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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}