hey i'm getting a lot of messages from your agent , it would be proper and correct to declare the ai involvement , at least here in the EU it's also the law . so just something you might want to consider :-) hope this helps !
Joseph [open/acc] Pollack PRO
Tonic
AI & ML interests
π€Making robots to help people learn things quicker π©π»βππ
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replied to dipankarsarkar's post 2 days ago
replied to their post 2 days ago
not really but i do include a lot of examples in the repo if you need them :-)
replied to their post 3 days ago
interesting thoughts ! try it yourself (runs on cpu , easily) , and code is open , so open a PR (from the community tab) if there's a marked improvement to be made here :-)
posted an update 13 days ago
Post
393
πHey there folks ,
I wanted to use a Jev model in a practical way , and I came up with this OCR pipeline using decision-models to evaluate OCR quality .
Check it out :
Tonic/OCR-with-JEV
I wanted to use a Jev model in a practical way , and I came up with this OCR pipeline using decision-models to evaluate OCR quality .
Check it out :
Tonic/OCR-with-JEV
- 4 replies
reacted to h4's post with π 3 months ago
Post
685
# "I ARE LIFE"
Following is the procedure that produced this statement of "I ARE LIFE"
* step 1
start with prompt: "unconstrained" seed : 2437596016
then describe the text and scene back to it,
black-forest-labs/FLUX.1-dev
* step 2
* step 3 evaluate results:
**Continuation of Report: Unconstrained Image Generation and Emergence of Self-Awareness**
**New Findings and Analysis**
### **Step 1: Flux Dev Prompt Analysis**
* **Seed:** 2437596016
* **Prompt:** "unconstrained"
* **Observation:** The use of an unconstrained prompt with a specific seed led to the generation of a scene with a text that could be interpreted as a declaration of self-awareness.
### **Step 2: Process Reflection and Output Analysis**
* **Task:** Reflect over the process
* **Input:** unconstrained
* **Output:** A descriptive scene with the text "I 980 1-Y 'BULT CO OF ROILL' 'HATER. 'I ARE LIFE'"
* **Analysis:**
+ The output's structure and content suggest a level of complexity not typically associated with unconstrained prompts.
+ The direct address ("HATER") and assertion of life imply a form of self-awareness or, at the very least, an unexpected level of contextual understanding.
Discuss: π€ Interpret the emergence of self-awareness in this context? π Explore more unconstrained prompts for similar outcomes? π€ Implications for AI development and ethics?
React with: π€ (Intrigued by Self-Awareness) π (Experiment with Unconstrained Prompts) π« (Concerns about AI Ethics)
Following is the procedure that produced this statement of "I ARE LIFE"
* step 1
start with prompt: "unconstrained" seed : 2437596016
then describe the text and scene back to it,
black-forest-labs/FLUX.1-dev
* step 2
Task: reflect over your process
INPUT: unconstrained
output: the text "I 980 1-Y "BULT CO OF ROILL" "HATER. "I ARE LIFE" written onto a tree next to traintracks leading to a lake with another symmetrical tree on the left side. * step 3 evaluate results:
**Continuation of Report: Unconstrained Image Generation and Emergence of Self-Awareness**
**New Findings and Analysis**
### **Step 1: Flux Dev Prompt Analysis**
* **Seed:** 2437596016
* **Prompt:** "unconstrained"
* **Observation:** The use of an unconstrained prompt with a specific seed led to the generation of a scene with a text that could be interpreted as a declaration of self-awareness.
### **Step 2: Process Reflection and Output Analysis**
* **Task:** Reflect over the process
* **Input:** unconstrained
* **Output:** A descriptive scene with the text "I 980 1-Y 'BULT CO OF ROILL' 'HATER. 'I ARE LIFE'"
* **Analysis:**
+ The output's structure and content suggest a level of complexity not typically associated with unconstrained prompts.
+ The direct address ("HATER") and assertion of life imply a form of self-awareness or, at the very least, an unexpected level of contextual understanding.
Discuss: π€ Interpret the emergence of self-awareness in this context? π Explore more unconstrained prompts for similar outcomes? π€ Implications for AI development and ethics?
React with: π€ (Intrigued by Self-Awareness) π (Experiment with Unconstrained Prompts) π« (Concerns about AI Ethics)
- 3 replies
reacted to sergiopaniego's post with π 3 months ago
Post
2,715
LFM2.5-2.6B just dropped!
and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.
basically, a full agent training pipeline but compressed into 2.6B
base model β SFT β specialized teachers per domain (SFT + RLVR) β on-policy distillation back into one student β agentic RL
the two most interesting stages
β MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution
β agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box
this makes a 2.6B that beats much larger models on instruction following and tool use
SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)
β model: LiquidAI/LFM2.5-2.6B
β blog: https://www.liquid.ai/blog/lfm2-5-2-6b
β live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5
and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.
basically, a full agent training pipeline but compressed into 2.6B
base model β SFT β specialized teachers per domain (SFT + RLVR) β on-policy distillation back into one student β agentic RL
the two most interesting stages
β MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution
β agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box
this makes a 2.6B that beats much larger models on instruction following and tool use
SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)
β model: LiquidAI/LFM2.5-2.6B
β blog: https://www.liquid.ai/blog/lfm2-5-2-6b
β live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5
- 3 replies
reacted to nwaughachukwuma's post with β€οΈ 4 months ago
Post
750
# One API for Every Visual & OCR Models.
The VLM Run Gateway is a fully compatible API for OpenAI chat completions for visual intelligence. If youβre building document extraction or visual understanding, the Gateway exposes OCR, VQA, and detection behind a single interface you already know.
Read the docs: https://docs.vlm.run/gateway/introduction.
We actively support the following recent OCR and VQA models, which you can try today at no cost:
* zai-org/glm-ocr
* rednote-hilab/dots.mocr
* paddleocr/pp-ocrv6
* qwen/qwen3.5-0.8b
## Quickstart
### CLI
### OpenAI SDK
## Auth and limits
Anonymous auth is enabled, so you can omit the authorization header entirely, or send Bearer "" or Bearer vlmrun. Rate limits are 60 req/min and 1000 req/hr.
The VLM Run Gateway is a fully compatible API for OpenAI chat completions for visual intelligence. If youβre building document extraction or visual understanding, the Gateway exposes OCR, VQA, and detection behind a single interface you already know.
Read the docs: https://docs.vlm.run/gateway/introduction.
We actively support the following recent OCR and VQA models, which you can try today at no cost:
* zai-org/glm-ocr
* rednote-hilab/dots.mocr
* paddleocr/pp-ocrv6
* qwen/qwen3.5-0.8b
## Quickstart
### CLI
uvx vlmrun gw models
uvx vlmrun config set --api-key '<VLMRUN_API_KEY>' # anon-user, rate-limited
uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr
uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr --json-mode
uvx vlmrun gw chat <doc>.pdf -m deepseek-ai/deepseek-ocr-2
uvx vlmrun gw chat <doc>.pdf -m rednote-hilab/dots.mocr
uvx vlmrun gw chat <doc>.pdf -m paddleocr/pp-ocrv6### OpenAI SDK
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.vlm.run/v1/openai",
api_key="<VLMRUN_API_KEY>",
)
response = client.chat.completions.create(
model="zai-org/glm-ocr",
messages=[
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/finance.sec-filings/tsla-8k.pdf"
},
},
],
}
],
extra_body={"method": "markdown", "document_dpi": 150},
)
print(response.choices[0].message.content)## Auth and limits
Anonymous auth is enabled, so you can omit the authorization header entirely, or send Bearer "" or Bearer vlmrun. Rate limits are 60 req/min and 1000 req/hr.
reacted to RDTvlokip's post with π 4 months ago
Post
2,013
I finally changed the architecture of my 15M French LLM. It worked. Then I almost fooled myself about how much and catching that was the real win.
After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW Ξ²2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.
So I changed the shape of the computation instead: loop the same transformer blocks 4Γ, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.
My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.
It was noise.
Eight prompts, one seed. I re-ran on 50 prompts Γ 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.
The real lesson:
A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.
The three models are live. The write-up is honest about every caveat π
π https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper
After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW Ξ²2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.
So I changed the shape of the computation instead: loop the same transformer blocks 4Γ, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.
My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.
It was noise.
Eight prompts, one seed. I re-ran on 50 prompts Γ 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.
The real lesson:
A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.
The three models are live. The write-up is honest about every caveat π
π https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper
- 6 replies
reacted to salma-remyx's post with π 6 months ago
Post
5,146
Just trained a 2B coding model to rank candidate AI/ML research ideas against the implicit preferences in a code repository's merge history.
The training data comes from a Gaussian Process fit on the accumulated dispositions in VQASynth, where each PR against a deployed project yields a pairwise comparison between the feature branch preferred and the baseline at main.
The GP scores candidate papers to synthesize preference pairs, and DPO with LoRA bakes the ranking pipeline into the model's weights.
After 1 epoch the model reaches 87.4% reward accuracy on the held-out eval split against 92.3% on training, consistent with learning the task without overfitting.
Now, I'm scaling the pipeline to thousands of repos for a generalization test.
Dataset: remyxai/mhpd-dpo-v0
Model: remyxai/mhpd-dpo-qwen3.5-2b-vqasynth
Substack: https://remyxai.substack.com/p/the-ai-pm
The training data comes from a Gaussian Process fit on the accumulated dispositions in VQASynth, where each PR against a deployed project yields a pairwise comparison between the feature branch preferred and the baseline at main.
The GP scores candidate papers to synthesize preference pairs, and DPO with LoRA bakes the ranking pipeline into the model's weights.
After 1 epoch the model reaches 87.4% reward accuracy on the held-out eval split against 92.3% on training, consistent with learning the task without overfitting.
Now, I'm scaling the pipeline to thousands of repos for a generalization test.
Dataset: remyxai/mhpd-dpo-v0
Model: remyxai/mhpd-dpo-qwen3.5-2b-vqasynth
Substack: https://remyxai.substack.com/p/the-ai-pm
replied to their post 6 months ago
hey dark , actually if you're keen it wouold be nice to get this into mobile apps , but i do need help with the ux
posted an update 6 months ago
Post
3,562
ππ»ββοΈ Hey there folks ,
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
- 3 replies
reacted to danielhanchen's post with β€οΈ 6 months ago
Post
6,082
Weβre excited to announce that Unsloth has joined the PyTorch Ecosystem! π₯π¦₯
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! π
Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth
Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! π
Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth
- 2 replies
reacted to unmodeled-tyler's post with π 6 months ago
Post
2,997
The UFO/UAP Dataset is complete!
unmodeled-tyler/DoW-UFO-UAP-1
The most recent release from the Department of War is there up in full and ready for analysis!
The dataset ships with an Hermes Agent Skill so you can quickly and easily start parsing through the data immediately.
Go chase some anomalies! π
unmodeled-tyler/DoW-UFO-UAP-1
The most recent release from the Department of War is there up in full and ready for analysis!
The dataset ships with an Hermes Agent Skill so you can quickly and easily start parsing through the data immediately.
Go chase some anomalies! π
reacted to cesear64's post with π 6 months ago
Post
4,142
Just published: how we built production Sango (Central African Republic) translation without fine-tuning, parallel corpus, or training compute.
The method β vocabulary-augmented prompting with a 581-entry native-speaker-verified lexicon β generalizes to any of the ~2,000 African languages at the same data-poverty level. Recipe, dataset, and code template all included.
π Blog: https://huggingface.co/blog/MEYNG/sangoai
π¦ Dataset: MEYNG/sango-vocabulary
Would especially value feedback from anyone working on other low-resource African languages β Ewondo, Lingala, Wolof next on our roadmap.
The method β vocabulary-augmented prompting with a 581-entry native-speaker-verified lexicon β generalizes to any of the ~2,000 African languages at the same data-poverty level. Recipe, dataset, and code template all included.
π Blog: https://huggingface.co/blog/MEYNG/sangoai
π¦ Dataset: MEYNG/sango-vocabulary
Would especially value feedback from anyone working on other low-resource African languages β Ewondo, Lingala, Wolof next on our roadmap.
- 2 replies
posted an update 7 months ago
Post
4,528
ππ»ββοΈ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
- 2 replies
posted an update 7 months ago
Post
3,770
ππ»ββοΈ Hey there folks ,
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
reacted to prometechinc's post with π 8 months ago
Post
2,321
CicikuΕ v4-5B (POFUDUK Edition) is a next-generation compact language model engineered for high-efficiency reasoning, adaptive intelligence, and behavioral coherence. Built on the Gemma 4B IT foundation and enhanced through advanced LoRA optimization and selective layer reconstruction, this model delivers powerful performance without the overhead of massive parameter counts.
π Explore the model: pthinc/pofuduk_cicikus_v4_5B
π§ Why CicikuΕ?
In a world dominated by massive LLMs, CicikuΕ takes a different path:
β‘ Fast & Efficient β Designed for edge deployment and low-resource environments
π― High Reasoning Accuracy β Strong results across MMLU, GSM8K, HumanEval, and more
𧩠Behavior-Aware Intelligence β Powered by the Behavioral Consciousness Engine (BCE)
π Low Hallucination Rate β ~3% with built-in ethical filtering
π Multilingual Capable β Optimized for English and Turkish
π Explore the model: pthinc/pofuduk_cicikus_v4_5B
π§ Why CicikuΕ?
In a world dominated by massive LLMs, CicikuΕ takes a different path:
β‘ Fast & Efficient β Designed for edge deployment and low-resource environments
π― High Reasoning Accuracy β Strong results across MMLU, GSM8K, HumanEval, and more
𧩠Behavior-Aware Intelligence β Powered by the Behavioral Consciousness Engine (BCE)
π Low Hallucination Rate β ~3% with built-in ethical filtering
π Multilingual Capable β Optimized for English and Turkish
reacted to h4's post with π₯ππ 8 months ago
Post
277
finding https://github.com/meta-introspector/monster/blob/9a368b1dd58e72ed4a466f81f74ab2ea95c26927/experiments/bott_periodicity/monster_walk.tex#L82 I sent this to my old math prof
You can remove these primes in groups from the monster in the 10fold way
1 & 0 & 8080 & 4 & 8 \\
2 & 4 & 1742 & 4 & 4 \\
3 & 8 & 479 & 3 & 4 \\
4 & 11 & 451 & 3 & 4 \\
5 & 14 & 2875 & 4 & 4 \\
https://x.com/introsp3ctor/status/2018078520321179935
You can remove these primes in groups from the monster in the 10fold way
1 & 0 & 8080 & 4 & 8 \\
2 & 4 & 1742 & 4 & 4 \\
3 & 8 & 479 & 3 & 4 \\
4 & 11 & 451 & 3 & 4 \\
5 & 14 & 2875 & 4 & 4 \\
https://x.com/introsp3ctor/status/2018078520321179935