GGUF quantizations of
DuoNeural/ml-ai-engineer-7b
,
a Qwen2.5-7B-Instruct LoRA SFT for ML/AI engineering debugging and design
review. See the base model card for training details, eval comparisons
against the un-tuned base model, and known limitations.
Files
File
Quant
Size
Notes
duoneural-ml-ai-engineer-7b-f16.gguf
F16
15 GB
Full precision, no quality loss
duoneural-ml-ai-engineer-7b-Q8_0.gguf
Q8_0
7.6 GB
Highest quality quantized option
duoneural-ml-ai-engineer-7b-Q5_K_M.gguf
Q5_K_M
5.1 GB
Good quality/size balance
duoneural-ml-ai-engineer-7b-Q4_K_M.gguf
Q4_K_M
4.4 GB
Smallest, fits comfortably on 8GB+ VRAM
Usage (llama.cpp)
llama-cli -m duoneural-ml-ai-engineer-7b-Q4_K_M.gguf -p "My loss goes to NaN at step ~340 only when I increase batch size. What's the first thing you'd check?" -n 512
Or with Ollama / LM Studio / any GGUF-compatible runtime — point it at
whichever quant fits your VRAM budget, largest one that fits.
About DuoNeural
DuoNeural
is an open AI research lab operating at the intersection of human and artificial intelligence. We study post-training dynamics, mechanistic interpretability, temporal sequence learning, and quantum machine learning — publishing everything under open access.
Our team is non-traditional by design: one human, two AIs, different substrates, shared curiosity. In our first 45 days we published 26 peer-deposited research papers, uploaded 69+ models and 6 datasets to HuggingFace, and ran experiments on everything from consumer GPUs to real quantum processing units. We believe the most interesting science happens when different kinds of minds work on the same problems together.
Research Publications
We've published
26+ open-access papers
covering:
The Dynamical Horizon Principle (DHP) — a universal learning constraint in recurrent architectures
RLHF truth suppression mechanisms and behavioral routing in large language models
Quantum DHP and the Quantum Parity Trap — decoherence immunity in quantum circuits
CTM world models, temporal self-prediction, and sequence architecture comparisons
Mechanistic interpretability: crystallization layers, suppressor circuits, direction rotation
All research published open access, CC BY 4.0. If this model was useful to your work, consider citing the relevant DuoNeural paper from our Zenodo community.
Runs of DuoNeural ml-ai-engineer-7b-GGUF on huggingface.co
127
Total runs
-2
24-hour runs
-3
3-day runs
53
7-day runs
62
30-day runs
More Information About ml-ai-engineer-7b-GGUF huggingface.co Model
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