HIT-TMG/VideoVista-2
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Papers
KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill
Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning
YunxinLi
updated a
dataset about 11 hours ago
Yuki131
updated a
collection 14 days ago
Yuki131
updated a
model 14 days ago
Yuki131
published a
model 14 days ago
Yuki131
updated a
collection 16 days ago
Yuki131
updated a
model 16 days ago
Yuki131
published a
model 16 days ago
Yuki131
updated a
collection 16 days ago
Yuki131
updated a
model 16 days ago
Yuki131
published a
model 17 days ago
Yuki131
published a
dataset 18 days ago
Yuki131
updated a
dataset 18 days ago
Post
2834
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
Post
3671
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
What language can the model speak?
1
#9 opened about 1 month ago
by 
xingjianll
YunxinLi
authored a
paper about 2 months ago
Move model files to repository root
#8 opened about 2 months ago
by 
xyidealist
Move model files to repository root
#8 opened about 2 months ago
by xyidealist
crazyofapple
authored a
paper about 2 months ago