Quantized GGUF versions of allenai/OLMo-2-1124-7B-Instruct — the Allen Institute for AI's fully open 7B language model. OLMo-2 is released under Apache 2.0 with full training data, code, intermediate checkpoints, and evaluation data all publicly available — the most transparent large language model at this scale.

Available Files

File	Quant	Size	Use Case
`OLMo-2-7B-Instruct-Q8_0.gguf`	Q8_0	~7.4GB	Maximum quality
`OLMo-2-7B-Instruct-Q6_K.gguf`	Q6_K	~5.7GB	Near-lossless
`OLMo-2-7B-Instruct-Q5_K_M.gguf`	Q5_K_M	~5.0GB	High quality
`OLMo-2-7B-Instruct-Q4_K_M.gguf`	Q4_K_M	~4.2GB	Recommended default
`OLMo-2-7B-Instruct-Q3_K_M.gguf`	Q3_K_M	~3.5GB	Low VRAM
`OLMo-2-7B-Instruct-IQ4_XS.gguf`	IQ4_XS	~3.8GB	Imatrix 4-bit
`OLMo-2-7B-Instruct-IQ3_XXS.gguf`	IQ3_XXS	~2.7GB	Imatrix 3-bit
`OLMo-2-7B-Instruct-IQ2_M.gguf`	IQ2_M	~2.4GB	Imatrix 2-bit
`OLMo-2-7B-Instruct-IQ1_S.gguf`	IQ1_S	~1.6GB	Extreme compression
`OLMo-2-7B-Instruct-fp16.gguf`	FP16	~14.0GB	Full precision
`imatrix.dat`	—	—	Importance matrix

Usage

bash

# llama.cpp
./llama-cli -m OLMo-2-7B-Instruct-Q4_K_M.gguf \
  --ctx-size 4096 -n 512 \
  -p "<|endoftext|><|user|>\nHello!<|assistant|>\n"

# Ollama
ollama run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

About OLMo-2-7B

Parameters: 7B
Context: 4096 tokens
Architecture: Modified decoder-only transformer (RoPE, SwiGLU, QK-norm)
Training Data: Dolmino Mix 1124 — fully open dataset
License: Apache 2.0 (commercial use OK)
Unique value: All artifacts open — weights, data, code, evals, checkpoints

OLMo-2 is the gold standard for research reproducibility in the 7B class. If you need a model where you can trace every decision back to first principles, this is it.

Quantized by DuoNeural using llama.cpp on RTX 5090.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Platform	Link
HuggingFace	huggingface.co/DuoNeural
Website	duoneural.com
GitHub	github.com/DuoNeural
X / Twitter	@DuoNeural
Email	duoneural@proton.me
Newsletter	duoneural.beehiiv.com
Support	buymeacoffee.com/duoneural

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804
The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems	10.5281/zenodo.19952612

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.