Phi 3.5 Vision
🔥
6
Analyze artwork and answer questions about images
Joseph Robert Turcotte PRO
Fishtiks
AI & ML interests
Roleplaying, lorabration, abliteration, smol models, extensive filtering, unusual datasets, home usage, HPCs for AI, distributed training/federated learning, and sentience.
AI should find and label AI hallucinations with GANs so we can give them context and use.
Recent Activity
liked
a Space 5 days ago
gizemsarsinlar/Phi-3.5-Artwork-Analysis liked
a Space 9 days ago
Lightricks/LTX-2-3 upvoted a paper 10 days ago
From Perception to Action: An Interactive Benchmark for Vision Reasoning Organizations
liked a Space 5 days ago
liked a Space 9 days ago
upvoted a paper 10 days ago
reacted to kostakoff's post with 🚀👍 10 days ago
Post
2078
Mining GPU Nvidia CMP 170HX - let's run some models!
To satisfy my curiosity, I investigated different GPUs and found this: a mining version of the A100 — the CMP 170HX.
It is a very interesting GPU. Based on public documentation, it has hardware similar to the datacenter A100. If you open it up and look at the board, you will see that it's very similar to an A100 board; it even has NVLink connectors.
Online, I found almost no information about how to run it, whether it works with LLMs, or if it's supported by default Nvidia drivers and CUDA. So, I decided to test it myself.
I installed it in my lab (see previous post https://huggingface.co/posts/kostakoff/584269728210158) and found that the default nvidia-driver-570 works with it out of the box. After that, I checked if CUDA was available, and it worked too.
The next step was to try running some models:
- Stable Diffusion XL with BNB4 quantization: It took around two minutes to generate an image, but it works!
- Compiled llama.cpp for CUDA (https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#compilation): I run Mistral 7B Q4_K_M, and this actually worked even better. It was able to generate 33 tokens per second and read 400 tokens per second.
There are some limitations related to power utilization:
- When running PyTorch, it doesn't utilize more than 80 watts.
- When running llama.cpp, utilization is a bit better but still limited to 113 watts.
I found this GitHub thread about the Nvidia CMP https://github.com/dartraiden/NVIDIA-patcher/issues/73, and it looks like this mining GPU has an internal rate limiter based on FMA compute calls. I haven't found a solution to bypass it yet.
llmlaba
To satisfy my curiosity, I investigated different GPUs and found this: a mining version of the A100 — the CMP 170HX.
It is a very interesting GPU. Based on public documentation, it has hardware similar to the datacenter A100. If you open it up and look at the board, you will see that it's very similar to an A100 board; it even has NVLink connectors.
Online, I found almost no information about how to run it, whether it works with LLMs, or if it's supported by default Nvidia drivers and CUDA. So, I decided to test it myself.
I installed it in my lab (see previous post https://huggingface.co/posts/kostakoff/584269728210158) and found that the default nvidia-driver-570 works with it out of the box. After that, I checked if CUDA was available, and it worked too.
The next step was to try running some models:
- Stable Diffusion XL with BNB4 quantization: It took around two minutes to generate an image, but it works!
- Compiled llama.cpp for CUDA (https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#compilation): I run Mistral 7B Q4_K_M, and this actually worked even better. It was able to generate 33 tokens per second and read 400 tokens per second.
There are some limitations related to power utilization:
- When running PyTorch, it doesn't utilize more than 80 watts.
- When running llama.cpp, utilization is a bit better but still limited to 113 watts.
I found this GitHub thread about the Nvidia CMP https://github.com/dartraiden/NVIDIA-patcher/issues/73, and it looks like this mining GPU has an internal rate limiter based on FMA compute calls. I haven't found a solution to bypass it yet.
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- 1 reply
Love it. I've been looking at these, and am glad there are some trials going.
liked 2 Spaces 14 days ago
liked a model 14 days ago
upvoted an article 15 days ago
Article
Space secrets security update
•
52
reacted to MikeDoes's post with 🚀🔥 19 days ago
Post
4549
At Ai4Privacy, our goal is to empower researchers to build a safer AI ecosystem. Today, we're highlighting crucial research that does just that by exposing a new vulnerability.
The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.
We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:
Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.
Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.
This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.
🔗 Read the full paper: https://arxiv.org/html/2408.17354v1
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.
We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:
Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.
Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.
This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.
🔗 Read the full paper: https://arxiv.org/html/2408.17354v1
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
liked a model 19 days ago
reacted to marksverdhei's post with 🔥 19 days ago
Post
1682
# The most underrated feature of Qwen3-TTS: Voice embeddings! 🧑🦰💬
https://huggingface.co/collections/marksverdhei/qwen3-voice-embedding
Did you know that Qwen3 TTS actually utilizes voice embedding?
Your voice is turned into a vector of 1024 (or 2048) dimensions,
and based on this vector alone you can get your custom voice.
But the coolest part is that this means that you can use math to modify voices, average voices. You can swap gender, pitch, mix and match vocies, and even create an emotion space! This also enables semantic voice search!
The voice embedding model is actually just a tiny encoder with just a few million parameters. I've ripped it out of the voice embeding model so you can use the embedding model standalone. Check out my collection! :D
https://huggingface.co/collections/marksverdhei/qwen3-voice-embedding
Did you know that Qwen3 TTS actually utilizes voice embedding?
Your voice is turned into a vector of 1024 (or 2048) dimensions,
and based on this vector alone you can get your custom voice.
But the coolest part is that this means that you can use math to modify voices, average voices. You can swap gender, pitch, mix and match vocies, and even create an emotion space! This also enables semantic voice search!
The voice embedding model is actually just a tiny encoder with just a few million parameters. I've ripped it out of the voice embeding model so you can use the embedding model standalone. Check out my collection! :D
- 1 reply
upvoted 2 papers 19 days ago
liked a dataset 19 days ago
reacted to Tonic's post with 🔥 21 days ago
Post
3244
🤔 Who would win ?
- a fully subsidized ai lab
- 3 random students named
kurakurai ?
demo : Tonic/fr-on-device
if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
- a fully subsidized ai lab
OR - 3 random students named
demo : Tonic/fr-on-device
if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
- 4 replies
liked a model 22 days ago
reacted to SeaWolf-AI's post with 🔥 22 days ago
Post
4278
FINAL Bench Released: The Real Bottleneck to AGI Is Self-Correction
We release FINAL Bench, the first benchmark for measuring functional metacognition in LLMs — the ability to detect and correct one's own reasoning errors. Every existing benchmark measures final-answer accuracy. None measures whether AI knows it is wrong.
Dataset: [FINAL-Bench/Metacognitive]( FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0
Leaderboard: FINAL-Bench/Leaderboard
Article: https://huggingface.co/blog/FINAL-Bench/metacognitive
Core Innovation
Our 5-axis rubric separates what no prior benchmark could: MA (Metacognitive Accuracy) — the ability to say "I might be wrong", and ER (Error Recovery) — the ability to actually fix it. This maps directly to the monitoring-control model of Nelson & Narens (1990) in cognitive psychology.
Three Findings Across 9 SOTA Models
We evaluated GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, DeepSeek-V3.2, Kimi K2.5, and others across 100 expert-level tasks:
1. ER Dominance. 94.8% of MetaCog gain comes from Error Recovery alone. The bottleneck to AGI is not knowledge or reasoning — it is self-correction.
2. Declarative-Procedural Gap. All 9 models can verbalize uncertainty (MA = 0.694) but cannot act on it (ER = 0.302). They sound humble but fail to self-correct — the most dangerous AI safety profile.
3. Difficulty Effect. Harder tasks benefit dramatically more from metacognition (Pearson r = -0.777, p < 0.001).
Paper: FINAL Bench: Measuring Functional Metacognitive Reasoning in LLMs
FINAL Bench is the first tool to tell apart what AI truly knows from what it merely pretends to know.
We release FINAL Bench, the first benchmark for measuring functional metacognition in LLMs — the ability to detect and correct one's own reasoning errors. Every existing benchmark measures final-answer accuracy. None measures whether AI knows it is wrong.
Dataset: [FINAL-Bench/Metacognitive]( FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0
Leaderboard: FINAL-Bench/Leaderboard
Article: https://huggingface.co/blog/FINAL-Bench/metacognitive
Core Innovation
Our 5-axis rubric separates what no prior benchmark could: MA (Metacognitive Accuracy) — the ability to say "I might be wrong", and ER (Error Recovery) — the ability to actually fix it. This maps directly to the monitoring-control model of Nelson & Narens (1990) in cognitive psychology.
Three Findings Across 9 SOTA Models
We evaluated GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, DeepSeek-V3.2, Kimi K2.5, and others across 100 expert-level tasks:
1. ER Dominance. 94.8% of MetaCog gain comes from Error Recovery alone. The bottleneck to AGI is not knowledge or reasoning — it is self-correction.
2. Declarative-Procedural Gap. All 9 models can verbalize uncertainty (MA = 0.694) but cannot act on it (ER = 0.302). They sound humble but fail to self-correct — the most dangerous AI safety profile.
3. Difficulty Effect. Harder tasks benefit dramatically more from metacognition (Pearson r = -0.777, p < 0.001).
from datasets import load_dataset
dataset = load_dataset("FINAL-Bench/Metacognitive", split="train")Paper: FINAL Bench: Measuring Functional Metacognitive Reasoning in LLMs
FINAL Bench is the first tool to tell apart what AI truly knows from what it merely pretends to know.
- 6 replies