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Model: jhu-clsp/rank1-llama3-8b-awq Source: Original Platform
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README.md
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---
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license: mit
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datasets:
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- jhu-clsp/rank1-training-data
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base_model:
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- jhu-clsp/rank1-llama3-8b
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pipeline_tag: text-generation
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tags:
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- reranker
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- retrieval
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- quantized
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- awq
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language:
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- en
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---
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# rank1-llama3-8b-awq: Quantized Model for Test-Time Compute Reranking
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📄 [Paper](https://arxiv.org/abs/2502.18418) | 🚀 [GitHub Repository](https://github.com/orionw/rank1)
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rank1-llama3-8b-awq is a quantized version of the rank1-llama3-8b model. This AWQ-quantized 8B parameter model maintains the reasoning capabilities of the original model while requiring less memory and providing faster inference. The model is trained from the Llama 3.1 8B base model and leverages test-time compute to generate reasoning chains before deciding if a document is relevant to a query.
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## Model Description
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rank1 introduces a novel approach to information retrieval by generating explicit reasoning chains before making relevance judgments. Unlike traditional rerankers that directly output scores, rank1:
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1. Receives a query and document pair
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2. Generates a reasoning chain within a `<think>...</think>` section
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3. Makes a binary relevance judgment (`true` or `false`)
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4. Returns a confidence score based on the logits of the true/false tokens
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This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks.
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## Quantization Details
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This model uses Activation-aware Weight Quantization (AWQ) to reduce the model size while maintaining performance. Compared to the full-precision model, this quantized version:
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- Requires less GPU memory
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- Offers faster inference times
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- Maintains comparable accuracy on retrieval tasks
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## Model Family
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| Model | Base | Description |
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|:------|:-----|:------------|
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| [rank1-7b](https://huggingface.co/jhu-clsp/rank1-7b) | Qwen2.5-7B | Qwen variant (7B parameters) |
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| [rank1-14b](https://huggingface.co/jhu-clsp/rank1-14b) | Qwen2.5-14B | Qwen variant (14B parameters) |
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| [rank1-32b](https://huggingface.co/jhu-clsp/rank1-32b) | Qwen2.5-32B | Qwen variant (32B parameters) |
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| [rank1-mistral-2501-24b](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b) | Mistral-Small 2501 24B | Mistral variant (24B parameters) |
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| [rank1-llama3-8b](https://huggingface.co/jhu-clsp/rank1-llama3-8b) | Llama 3.1 8B | Full-precision version (8B parameters) |
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### Quantized Variants
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| Model | Description |
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|:------|:------------|
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| [rank1-7b-awq](https://huggingface.co/jhu-clsp/rank1-7b-awq) | Quantized version of rank1-7b |
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| [rank1-14b-awq](https://huggingface.co/jhu-clsp/rank1-14b-awq) | Quantized version of rank1-14b |
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| [rank1-32b-awq](https://huggingface.co/jhu-clsp/rank1-32b-awq) | Quantized version of rank1-32b |
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| [rank1-mistral-2501-24b-awq](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b-awq) | Quantized version of rank1-mistral-24b |
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| [rank1-llama3-8b-awq](https://huggingface.co/jhu-clsp/rank1-llama3-8b-awq) | Current model - Quantized version of rank1-llama3-8b |
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## Associated Data and Resources
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| Resource | Description |
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|:---------|:------------|
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| [rank1-r1-msmarco](https://huggingface.co/datasets/jhu-clsp/rank1-r1-msmarco) | All R1 output examples from MS MARCO |
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| [rank1-training-data](https://huggingface.co/datasets/jhu-clsp/rank1-training-data) | Training data used for rank1 models |
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| [rank1-run-files](https://huggingface.co/datasets/jhu-clsp/rank1-run-files) | Pre-computed run files for use in top 100 doc reranking |
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| [GitHub Repository](https://github.com/orionw/rank1) | Official rank1 repository |
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## Usage
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Note that official usage is found on the Github and accounts for edge cases. But for simple use cases the minimal example below works.
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<details>
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<summary>Click to expand: Minimal example with vLLM</summary>
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```python
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from vllm import LLM, SamplingParams
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import math
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# Initialize the model with vLLM
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model = LLM(
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model="jhu-clsp/rank1-llama3-8b-awq",
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tensor_parallel_size=1, # Number of GPUs
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trust_remote_code=True,
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max_model_len=16000, # Context length
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gpu_memory_utilization=0.9,
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dtype="auto", # Will use the appropriate quantized dtype
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)
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# Set up sampling parameters
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sampling_params = SamplingParams(
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temperature=0,
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max_tokens=8192,
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logprobs=20,
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stop=["</think> true", "</think> false"],
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skip_special_tokens=False
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)
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# Prepare the prompt
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def create_prompt(query, document):
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return (
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"Determine if the following passage is relevant to the query. "
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"Answer only with 'true' or 'false'.\n"
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f"Query: {query}\n"
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f"Passage: {document}\n"
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"<think>"
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)
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# Example usage
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query = "What are the effects of climate change?"
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document = "Climate change leads to rising sea levels, extreme weather events, and disruptions to ecosystems. These effects are caused by increasing greenhouse gas concentrations in the atmosphere due to human activities."
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# Generate prediction
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prompt = create_prompt(query, document)
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outputs = model.generate([prompt], sampling_params)
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# Extract score
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output = outputs[0].outputs[0]
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text = output.text
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final_logits = output.logprobs[-1]
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# Get token IDs for "true" and "false" tokens
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-llama3-8b-awq")
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true_token = tokenizer(" true", add_special_tokens=False).input_ids[0]
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false_token = tokenizer(" false", add_special_tokens=False).input_ids[0]
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# Calculate relevance score (probability of "true")
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true_logit = final_logits[true_token].logprob
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false_logit = final_logits[false_token].logprob
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true_score = math.exp(true_logit)
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false_score = math.exp(false_logit)
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relevance_score = true_score / (true_score + false_score)
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print(f"Reasoning chain: {text}")
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print(f"Relevance score: {relevance_score}")
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```
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</details>
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<details>
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<summary>Click to expand: Usage with AutoGPTQ/AWQ</summary>
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load the tokenizer and quantized model
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tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-llama3-8b-awq")
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model = AutoModelForCausalLM.from_pretrained(
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"jhu-clsp/rank1-llama3-8b-awq",
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device_map="auto",
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trust_remote_code=True
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)
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# Prepare the prompt
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query = "What are the effects of climate change?"
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document = "Climate change leads to rising sea levels, extreme weather events, and disruptions to ecosystems. These effects are caused by increasing greenhouse gas concentrations in the atmosphere due to human activities."
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prompt = f"Determine if the following passage is relevant to the query. Answer only with 'true' or 'false'.\nQuery: {query}\nPassage: {document}\n<think>"
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# Generate the reasoning chain and relevance judgment
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.0,
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return_dict_in_generate=True,
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output_scores=True,
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pad_token_id=tokenizer.eos_token_id
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)
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# Process the output
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generated_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=False)
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reasoning_chain = generated_text.split("<think>")[1].split("</think>")[0].strip()
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relevance_judgment = "true" if "true" in generated_text.split("</think>")[1].strip().lower() else "false"
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print(f"Reasoning chain: {reasoning_chain}")
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print(f"Relevance judgment: {relevance_judgment}")
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```
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</details>
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## Performance
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rank1-llama3-8b-awq demonstrates strong performance on retrieval benchmarks while offering faster inference and lower memory requirements than the full-precision model. The quantization process preserves the model's ability to "think through" relevance decisions, making it effective for nuanced topics.
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For specific benchmark results and comparisons with other models, please refer to the paper and the official GitHub repository.
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## Installation
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Please see the Github for detailed installation instructions.
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## MTEB Integration
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rank1 is compatible with the [MTEB benchmarking framework](https://github.com/embeddings-benchmark/mteb):
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```python
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from mteb import MTEB
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from rank1 import rank1 # From the official repo
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# Initialize the model
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model = rank1(
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model_name_or_path="jhu-clsp/rank1-llama3-8b-awq",
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num_gpus=1,
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device="cuda",
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quantized=True # Indicate that you're using the quantized version
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)
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# Run evaluation on specific tasks
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evaluation = MTEB(tasks=["NevIR"])
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results = evaluation.run(model)
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```
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## Citation
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If you use rank1 in your research, please cite our work:
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```bibtex
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@misc{weller2025rank1testtimecomputereranking,
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title={Rank1: Test-Time Compute for Reranking in Information Retrieval},
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author={Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme},
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year={2025},
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eprint={2502.18418},
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archivePrefix={arXiv},
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primaryClass={cs.IR},
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url={https://arxiv.org/abs/2502.18418},
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}
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```
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## License
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[MIT License](https://github.com/orionw/rank1/blob/main/LICENSE)
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config.json
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"modules_to_not_convert": null,
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"quant_method": "awq",
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"version": "gemm",
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"zero_point": true
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},
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"rms_norm_eps": 1e-05,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.47.1",
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"use_cache": false,
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"vocab_size": 128320
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.47.1"
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}
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6
quant_config.json
Normal file
6
quant_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
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|
||||
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|
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|
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"version": "GEMM"
|
||||
}
|
||||
27
special_tokens_map.json
Normal file
27
special_tokens_map.json
Normal file
@@ -0,0 +1,27 @@
|
||||
{
|
||||
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|
||||
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|
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|
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}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:0eb85735b2f18c464a3a3854c1f6e06f0c2cee200359d38ccd3e4667c947cdc0
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size 17210330
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2086
tokenizer_config.json
Normal file
2086
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user