Text Classification
PEFT
Safetensors
English
reranker
memory-retrieval
long-term-memory
dialog
lora
distillation
lycheemem
Instructions to use fuhao23/reranker_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fuhao23/reranker_v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-Reranker-0.6B") model = PeftModel.from_pretrained(base_model, "fuhao23/reranker_v1") - Notebooks
- Google Colab
- Kaggle
| {%- set instruction = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("Given a web search query, retrieve relevant passages that answer the query") -%} | |
| {%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%} | |
| {%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%} | |
| <|im_start|>system | |
| Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|> | |
| <|im_start|>user | |
| <Instruct>: {{ instruction }} | |
| <Query>: {{ query_text }} | |
| <Document>: {{ document_text }}<|im_end|> | |
| <|im_start|>assistant | |
| <think> | |
| </think> | |