license, base_model, tags, language, pipeline_tag

license	base_model	tags	language	pipeline_tag
apache-2.0	allenai/OLMo-2-1124-7B-Instruct	gguf quantized olmo allenai text-generation fully-open	en	text-generation

OLMo-2-7B-Instruct — GGUF Quants

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

# 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.

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Quantized by DuoNeural using llama.cpp on RTX 5090.

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DuoNeural

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

Platform	Link
HuggingFace	huggingface.co/DuoNeural
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GitHub	github.com/DuoNeural
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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.