Lychee-KaLM-Reranker&Jev
Collection
18 items • Updated • 5
AI & ML interests
Building the foundation for better search with embeddings, rerankers, RAG infrastructure, and small language models.
Recent Activity
Papers
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
LMEB: Long-horizon Memory Embedding Benchmark
Yuki131
updated a
collection about 4 hours ago
Post
2791
Meet JevEmbed: an open-source framework for embedding-based decisions
Turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.
We’ve open-sourced JevEmbed, a Python framework for three structured decision tasks:
🎯 Choice: select from a set of candidates
📊 Score: rate against ordered criteria
✅ Noul: judge whether a statement or question holds
🔧 JevEmbed currently includes configurations for KaLM, Qwen3, and E5 embedding models. You can use it through a Python API, CLI, or optional HTTP server. It also supports local LoRA fine-tuning, so you can adapt an embedding model to your own decision tasks and load the resulting adapter for local inference.
Fine-tuning results
📈 We trained KaLM-Embedding-V2.5 and Qwen3-Embedding-0.6B on the 79,116-example training split of Open-Jev’s release-v2-redistributable subset. We then evaluated them on 3,495 hard-label questions from the same subset’s held-out validation split.
ZefanCai/Open-Jev
KaLM-Embedding-V2.5: 30.24% base accuracy → 76.68% after LoRA fine-tuning
Qwen3-Embedding-0.6B: 30.73% base accuracy → 84.06% after LoRA fine-tuning
KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5
Qwen/Qwen3-Embedding-0.6B
These results are specific to that validation split. Performance on other tasks and datasets may differ.
JevEmbed also supports Choice tasks with more than 255 candidates, making it useful for classification and routing problems with large candidate sets.
Explore the framework, open an issue, or tell us what decision task you would try it on:
🔗 https://github.com/HITsz-TMG/JevEmbed
#Embeddings #LoRA #SentenceTransformers #OpenSource #JevEmbed
Turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.
We’ve open-sourced JevEmbed, a Python framework for three structured decision tasks:
🎯 Choice: select from a set of candidates
📊 Score: rate against ordered criteria
✅ Noul: judge whether a statement or question holds
🔧 JevEmbed currently includes configurations for KaLM, Qwen3, and E5 embedding models. You can use it through a Python API, CLI, or optional HTTP server. It also supports local LoRA fine-tuning, so you can adapt an embedding model to your own decision tasks and load the resulting adapter for local inference.
Fine-tuning results
📈 We trained KaLM-Embedding-V2.5 and Qwen3-Embedding-0.6B on the 79,116-example training split of Open-Jev’s release-v2-redistributable subset. We then evaluated them on 3,495 hard-label questions from the same subset’s held-out validation split.
ZefanCai/Open-Jev
KaLM-Embedding-V2.5: 30.24% base accuracy → 76.68% after LoRA fine-tuning
Qwen3-Embedding-0.6B: 30.73% base accuracy → 84.06% after LoRA fine-tuning
KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5
Qwen/Qwen3-Embedding-0.6B
These results are specific to that validation split. Performance on other tasks and datasets may differ.
JevEmbed also supports Choice tasks with more than 255 candidates, making it useful for classification and routing problems with large candidate sets.
Explore the framework, open an issue, or tell us what decision task you would try it on:
🔗 https://github.com/HITsz-TMG/JevEmbed
#Embeddings #LoRA #SentenceTransformers #OpenSource #JevEmbed
- 3 replies
Yuki131
updated 3
models 8 days ago
Yuki131
updated 3
models 10 days ago
Yuki131
updated a
collection 10 days ago
Post
3630
Meet KaLM-Jev — your local, Jev-style judgment engine, available in Nano, Small, and Large.
Building an agent or automation workflow? Sometimes all you need is a choice, a score, or a signal that a condition holds.
Built on KaLM-Reranker-R2, KaLM-Jev turns these decisions into structured outputs through three primitives:
🔀 Choice — select among candidates, with a probability distribution.
📊 Score — return a continuous score over your defined levels.
🔍 Noul — evaluate conditions independently, so multiple conditions can hold at once.
Think support-ticket routing, bug severity scoring, human-escalation detection, or candidate tool selection for agents.
🖥️ Run locally with downloaded weights
📦 Choose from Nano / Small / Large
🔌 Integrate through HTTP or Python
⚡ Reuse cached candidate/rule representations to reduce repeated encoding
🧪 Explore included examples, bilingual semantic smoke tests, and recorded GPU validation results
No answer-text generation:
KaLM-Jev is an independent implementation based on KaLM-Reranker, not an official TypeSafe project or a guarantee of full Jev compatibility. Scores are uncalibrated; validate thresholds on your own tasks.
Code & quickstart:
https://github.com/KaLM-Embedding/KaLM-Jev
Yuki131/KaLM-Jev
We’d love to hear what you’d build with it. Try it out, share feedback, or open an issue! 🤗
#Jev #Reranker #Agents #LocalAI #OpenSource
Building an agent or automation workflow? Sometimes all you need is a choice, a score, or a signal that a condition holds.
Built on KaLM-Reranker-R2, KaLM-Jev turns these decisions into structured outputs through three primitives:
🔀 Choice — select among candidates, with a probability distribution.
📊 Score — return a continuous score over your defined levels.
🔍 Noul — evaluate conditions independently, so multiple conditions can hold at once.
Think support-ticket routing, bug severity scoring, human-escalation detection, or candidate tool selection for agents.
🖥️ Run locally with downloaded weights
📦 Choose from Nano / Small / Large
🔌 Integrate through HTTP or Python
⚡ Reuse cached candidate/rule representations to reduce repeated encoding
🧪 Explore included examples, bilingual semantic smoke tests, and recorded GPU validation results
No answer-text generation:
output_tokens = 0. Inference still runs to compute the judgments.KaLM-Jev is an independent implementation based on KaLM-Reranker, not an official TypeSafe project or a guarantee of full Jev compatibility. Scores are uncalibrated; validate thresholds on your own tasks.
Code & quickstart:
https://github.com/KaLM-Embedding/KaLM-Jev
Yuki131/KaLM-Jev
We’d love to hear what you’d build with it. Try it out, share feedback, or open an issue! 🤗
#Jev #Reranker #Agents #LocalAI #OpenSource
- 1 reply
Yuki131
published 3
models 10 days ago
cosyy
updated 4
models 14 days ago
KaLM-Embedding/KaLM-Reranker-V1-Nano
Text Ranking • 0.8B • Updated • 1.08k • 10
KaLM-Embedding/KaLM-Reranker-V1-Large-R2
Text Ranking • 8B • Updated • 66 • 2
KaLM-Embedding/KaLM-Reranker-V1-Small-R2
Text Ranking • 2B • Updated • 153 • 3
KaLM-Embedding/KaLM-Reranker-V1-Nano-R2
Text Ranking • 0.8B • Updated • 280 • 2