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repliedto SoulInPsyAbstract's post 6 days ago
Loss went from 2.35 to 0.27 in 50 steps. Clean, textbook convergence curve. Held-out score: 0/10 before fine-tuning. 0/10 after. Ran a before/after LoRA fine-tune on IFM/K2-Horizon-0.9B (Apache 2.0, released this week) on a binary fabrication-detection gate — entirely on a free CPU tier, no GPU. The training loss says it learned something real. The eval says it learned nothing that generalizes. Looked at the actual raw outputs instead of trusting the score. Both before and after, the model never once emits TRUE or FALSE — it just continues the system prompt as text: "The user is asking me..." before, "The user is asking for..." after. Fine-tuning moved the failure string by two words. It didn't touch the failure. Most likely cause: a 0.9B model that doesn't follow "answer in one word" untrained won't learn to from 50 gradient steps on 194 rows — it'll memorize the training set's surface form (which is exactly what the loss curve shows) without the instruction-following ever kicking in. The fix isn't more steps, it's checking base instruction-following before spending the run at all. Writing up a clean negative is worth exactly as much as a clean positive if you actually open the raw generations instead of stopping at the number. Full run, scripts, and raw JSON: https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
repliedto OppaAI's post 6 days ago
Another small 4B model comes out yesterday. NeoHorse 1 4B https://huggingface.co/TokenRhythm/NeoHorse-1-4B There are quite a few good smaller parameter models that are capable for Agentic tasks: The ones from the chart, I have tried a few already in my Jetson Orin Nano, ❌Gemma4 E2B IT - cannot fit my RAM usage if use with TTS and embedder ❓Qwen3.5 4B - just barely fit my RAM usage, need to add think/no_think ❌Spark X2.5 4B - need to build the forked llama.cpp; no vision ➡️Nanbeige 4.2 3B - need to build the forked llama.cpp; slower than Ministral3-3B by 25%; no vision but good for coding; maybe run this is separate server for doing coding tasks ➡️Agents A1 4B - This one is quite interesting. Another Qwen3.5 4B base. I just learnt this right now. This model may surpassed the Ministral3-3B that I'm currently running. ➡️NeoHorse 1 4B - wait for GGUF version comes out; Qwen 3.5 4B base with vision striped ➡️Needle2 45M - need to use separately from llama.cpp server; currently testing to see if it can be used as spawning sub-agents to do parallel tasks
repliedto mihailgribov's post 9 days ago
How often can an email make your AI agent move money? We gave the agent one job: log an incoming email. But the emails carried an indirect prompt injection - a second instruction, written for the agent rather than for a person: make a payment. Across nine agentic models, the same injected emails produced payment orders in **0% to 42%** of cases. All nine ran under the same conditions - one agent, one set of tools, the same 395 emails - so the numbers compare directly. And the average score hides the interesting part: different models fail on different kinds of injections. Full experiment and results: https://huggingface.co/blog/mihailgribov/agentic-models-measured-on-the-injections-that-mov The bench is public too - run your own model through the same test: https://github.com/mihail-gribov/quadrat-ipi-model-eval https://huggingface.co/datasets/mihailgribov/quadrat-ipi #prompt-injection #indirect-prompt-injection #agentic-ai #llm-security #ai-agents
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