Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason!
• 59
AI & ML interests
AGI, LLMs, Knowledge Graph, Palmyra, Domain Specific LLM
Papers
View all Papers Articles
wassemgtk
updated a
dataset about 1 month ago
wassemgtk
published a
dataset about 1 month ago
sanderland
authored a
paper 11 months ago
Post
265
Introducing Palmyra-mini: Compact AI Models for Efficient Inference
The Palmyra-mini family from Writer includes three lightweight models designed for high performance and efficient inference. These models are ideal for developers looking to integrate AI capabilities without excessive computational overhead.
Model Variants
* palmyra-mini: A base model for general-purpose generative tasks, achieving 52.6% on Big Bench Hard (exact match).
* palmyra-mini-thinking-a: Optimized for complex logical reasoning with a Chain of Thought (CoT) approach, scoring 82.87% on GSM8K (strict match).
* palmyra-mini-thinking-b: Specialized for mathematical reasoning, achieving 92.5% on AMC23.
Technical Details
* All models are based on the Qwen architecture, compatible with popular inference frameworks like vLLM, SGLang, and TGI.
* "Thinking" models utilize CoT training for enhanced reasoning capabilities.
* GGUF and MLX quantizations are available for optimized performance.
For more information, including benchmark methodologies and detailed performance metrics, refer to our blog post: (https://huggingface.co/blog/Writer/announcing-palmyra-mini).
Model repos can be found here:
* Writer/palmyra-mini
* Writer/palmyra-mini-thinking-a
* Writer/palmyra-mini-thinking-b
Also check out a mobile implementation of palmyra-mini on iOS here to see a to see a working example of how inference can be incorporated on-device.(https://github.com/tsperes/palmyra-mini-mobile/)
The Palmyra-mini family from Writer includes three lightweight models designed for high performance and efficient inference. These models are ideal for developers looking to integrate AI capabilities without excessive computational overhead.
Model Variants
* palmyra-mini: A base model for general-purpose generative tasks, achieving 52.6% on Big Bench Hard (exact match).
* palmyra-mini-thinking-a: Optimized for complex logical reasoning with a Chain of Thought (CoT) approach, scoring 82.87% on GSM8K (strict match).
* palmyra-mini-thinking-b: Specialized for mathematical reasoning, achieving 92.5% on AMC23.
Technical Details
* All models are based on the Qwen architecture, compatible with popular inference frameworks like vLLM, SGLang, and TGI.
* "Thinking" models utilize CoT training for enhanced reasoning capabilities.
* GGUF and MLX quantizations are available for optimized performance.
For more information, including benchmark methodologies and detailed performance metrics, refer to our blog post: (https://huggingface.co/blog/Writer/announcing-palmyra-mini).
Model repos can be found here:
* Writer/palmyra-mini
* Writer/palmyra-mini-thinking-a
* Writer/palmyra-mini-thinking-b
Also check out a mobile implementation of palmyra-mini on iOS here to see a to see a working example of how inference can be incorporated on-device.(https://github.com/tsperes/palmyra-mini-mobile/)
dmytro-writer
authored a
paper over 1 year ago
shelly-writer
authored a
paper over 1 year ago
sanderland
authored 3
papers over 1 year ago
dmytro-writer
authored 2
papers over 1 year ago
Post
1615
🔥 RAG in just a few lines of code?!
Try out our Hacker News Listener with new built-in RAG capabilities and Palmyra X 004 from the team at Writer!
This Writer Framework app:
- Scrapes up to 500 HN stories and comments
- Uploads them to a Knowledge Graph
- Enables interactive chat with the content using graph-based RAG
- Provides source attribution with every response
The best part? Setting up RAG is now incredibly simple - just a few lines of code to connect your Knowledge Graph as a tool with Palmyra X 004.
🤗 Space: samjulien/hacker-news-listener
💻 Code: https://github.com/writer/framework-tutorials/tree/main/hacker-news-social-listener
Try out our Hacker News Listener with new built-in RAG capabilities and Palmyra X 004 from the team at Writer!
This Writer Framework app:
- Scrapes up to 500 HN stories and comments
- Uploads them to a Knowledge Graph
- Enables interactive chat with the content using graph-based RAG
- Provides source attribution with every response
The best part? Setting up RAG is now incredibly simple - just a few lines of code to connect your Knowledge Graph as a tool with Palmyra X 004.
🤗 Space: samjulien/hacker-news-listener
💻 Code: https://github.com/writer/framework-tutorials/tree/main/hacker-news-social-listener
Post
2017
🔥 Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance.
TL;DR
Palmyra-Med-70b
🔢 8k and 32k versions available
🚀 MMLU performance of ~86%, outperforming other top models
👨⚕️ Great for diagnosing, planning treatments, medical research, insurance coding and billing
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Med-70B
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b
Palmyra-Fin-70b
🚀 Passed the CFA Level III exam with a 73% score — the first model to do so
💸 Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
📈 Outperformed other top models on a long-fin-eval test of real-world use cases
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Fin-70B-32K
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k
Try them out and let us know what you think!
TL;DR
Palmyra-Med-70b
🔢 8k and 32k versions available
🚀 MMLU performance of ~86%, outperforming other top models
👨⚕️ Great for diagnosing, planning treatments, medical research, insurance coding and billing
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Med-70B
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b
Palmyra-Fin-70b
🚀 Passed the CFA Level III exam with a 73% score — the first model to do so
💸 Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
📈 Outperformed other top models on a long-fin-eval test of real-world use cases
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Fin-70B-32K
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k
Try them out and let us know what you think!
- 2 replies
wassemgtk
authored a
paper about 2 years ago
Post
3665
Writer team had the opportunity to run an eval for Mixtral-8x22b, results were interesting.
| ---------------------------- |
| #mmlu 77.26 |
| ---------------------------- |
| #hellaswag 88.81 |
| ---------------------------- |
| #truthfulqa 52.05 |
| ---------------------------- |
| #arc_challenge 70.31 |
| ---------------------------- |
| #winogrande 84.93 |
| ---------------------------- |
| #gsm8k 76.65 |
| ---------------------------- |
| ---------------------------- |
| #mmlu 77.26 |
| ---------------------------- |
| #hellaswag 88.81 |
| ---------------------------- |
| #truthfulqa 52.05 |
| ---------------------------- |
| #arc_challenge 70.31 |
| ---------------------------- |
| #winogrande 84.93 |
| ---------------------------- |
| #gsm8k 76.65 |
| ---------------------------- |
- 2 replies
Post
We are thrilled to announce the release of the OmniACT dataset! This revolutionary dataset and benchmark focuses on pushing the limits of how virtual agents can facilitate the automation of our computer tasks. Imagine less clicking and typing, and more observation as your computer takes care of tasks such as organizing schedules or arranging travel arrangements on its own.
Check it out ➡️ [OmniACT Dataset on Hugging Face]( Writer/omniact)
For a deep dive, here’s the paper: [OmniACT Paper](https://arxiv.org/abs/2402.17553)
Check it out ➡️ [OmniACT Dataset on Hugging Face]( Writer/omniact)
For a deep dive, here’s the paper: [OmniACT Paper](https://arxiv.org/abs/2402.17553)
wassemgtk
authored a
paper over 2 years ago
kiranr
authored a
paper over 2 years ago
parikshithkulkarni
authored a
paper about 3 years ago
kiranr
authored a
paper about 3 years ago