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https://www.tonic-ai.com
josephpollack
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🤖🤗multi media inputs and outputs to create augmented culture and better outcomes for humans everywhere.❤️🚀

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Narchethan  updated a Space about 1 month ago
MultiTransformer/Orion
Tonic  updated a Space about 1 month ago
MultiTransformer/tonic_gradio_bot
Narchethan  published a Space about 1 month ago
MultiTransformer/Orion
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MultiTransformer's activity

daavoo 
posted an update 4 days ago
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2723
Wondering how the new Google Agent Development Toolkit (ADK) compares against other frameworks? 🤔You can try it in any-agent 🚀

https://github.com/mozilla-ai/any-agent

agent = AnyAgent.create(
    AgentFramework("google"),
    AgentConfig(
        model_id="gpt-4o-mini"
    )
)
agent.run("Which Agent Framework is the best??")

  • 1 reply
·
hesamation 
posted an update 5 days ago
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6649
Google published a 69-page whitepaper on Prompt Engineering and its best practices, a must-read if you are using LLMs in production:
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct

LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
daavoo 
posted an update 6 days ago
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1802
After working on agent evaluation🔍🤖 the last weeks, we started to accumulate code to make trying different agent frameworks easier. From that code, we have built and just released a small library called any-agent.


Give it a try and a ⭐: https://github.com/mozilla-ai/any-agent

from any_agent import AgentConfig, AgentFramework, AnyAgent

agent = AnyAgent.create(
    AgentFramework("smolagents"),  # or openai, langchain, llama_index
    AgentConfig(
        model_id="gpt-4o-mini"
    )
)
agent.run("Which Agent Framework is the best??")
AtAndDev 
posted an update 8 days ago
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2865
Llama 4 is out...
·
hesamation 
posted an update 9 days ago
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2788
The best researchers from Yale, Stanford, Google DeepMind, and Microsoft laid out all we know about Agents in a 264-page paper [book],

Here are some of their key findings:

They build a mapping of different agent components, such as perception, memory, and world modelling, to different regions of the human brain and compare them:

- brain is much more energy-efficient
- no genuine experience in agents
- brain learns continuously, agent is static

An agent is broken down to:
- Perception: the agent's input mechanism. can be improved with multi-modality, feedback mechanisms (e.g., human corrections), etc.
- Cognition: learning, reasoning, planning, memory. LLMs are key in this part.
- Action: agent's output and tool use.

Agentic memory is represented as:
- Sensory memory or short-term holding of inputs which is not emphasized much in agents.
- Short-term memory which is the LLM context window
- Long-term memory which is the external storage such as RAG or knowledge graphs.

The memory in agents can be improved and researched in terms of:
- increasing the amount of stored information
- how to retrieve the most relevant info
- combining context-window memory with external memory
- deciding what to forget or update in memory

The agent must simulate or predict the future states of the environment for planning and decision-making.

ai world models are much simpler than the humans' with their causal reasoning (cause-and-effect) or physical intuition.

LLM world models are mostly implicit and embedded.

EMOTIONS are a deep aspect of humans, helping them with social interactions, decision-making, or learning.

Agents must understand emotions to better interact with us.

But rather than encoding the feeling of emotions, they have a surface-level modelling of emotions.

Perception is the process by which an agent receives and interprets raw data from its surroundings.

READ PAPER: Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems (2504.01990)
hesamation 
posted an update 13 days ago
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2675
What, How, Where, and How Well? This paper reviews test-time scaling methods and all you need to know about them:
> parallel, sequential, hybrid, internal scaling
> how to scale (SFT, RL, search, verification)
> metrics and evals of test-time scaling

🔗paper: What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models (2503.24235)

If you want to learn what inference-time compute scaling is @rasbt has a great blog post on that:
https://magazine.sebastianraschka.com/p/state-of-llm-reasoning-and-inference-scaling
hesamation 
posted an update 14 days ago
daavoo 
posted an update 25 days ago
AtAndDev 
posted an update 29 days ago
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4200
There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...
  • 6 replies
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daavoo 
posted an update about 1 month ago
AtAndDev 
posted an update about 1 month ago
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1599
Gemma 3 seems to be really good at human preference. Just waiting for ppl to see it.
not-lain 
posted an update about 1 month ago
Narchethan 
updated a Space about 1 month ago
Tonic 
updated a Space about 1 month ago
Narchethan 
published a Space about 1 month ago
Tonic 
published a Space about 1 month ago
daavoo 
posted an update about 1 month ago
Tonic 
posted an update about 1 month ago
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1295
🙋🏻‍♂️Hey there folks,

Did you know that you can use ModernBERT to detect model hallucinations ?

Check out the Demo : Tonic/hallucination-test

See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard

check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1

and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect

👆🏻if you like this topic please contribute code upstream 🚀

  • 2 replies
·
Tonic 
published a Space about 1 month ago
Tonic 
updated a Space about 1 month ago