---

license: apache-2.0

base_model: allenai/BAR-5x7B

tags:

• allenai
• bar
• flex-olmo
• olmo
• moe
• mixture-of-experts
• gguf
• llama.cpp
• quantized
• q4_k_m
• 16gb
• 24gb

language:

• en

pipeline_tag: text-generation

library_name: gguf

base_model_relation: quantized

---

BAR-5x7B — GGUF (first-of-its-kind FlexOlmo conversion)

This is the first GGUF conversion of allenai/BAR-5x7B, the largest member of AllenAI's BAR-family Mixture-of-Experts models released on 2026-04-19 based on the new FlexOlmo architecture.

5 experts × 7B → ~33B total parameters with top-k routing.

⚠ Requires patched llama.cpp

The FlexOlmo architecture is not yet supported in upstream llama.cpp. To run this GGUF use the FlexOlmo support fork:

• Fork: https://github.com/Seraphiel102/llama.cpp/tree/flex-olmo-pr-clean

Build from the fork:

git clone https://github.com/Seraphiel102/llama.cpp.git
cd llama.cpp
git checkout flex-olmo-pr-clean
cmake -B build -DGGML_CUDA=OFF
cmake --build build -j --target llama-cli llama-quantize llama-completion

What FlexOlmo is

Per transformers.models.flex_olmo, FlexOlmoDecoderLayer is Olmo2's hybrid post-norm decoder layer with the dense FFN swapped for OlmoE-style top-k MoE routing. Specifically:

• Attention with q_norm and k_norm (Olmo2-style)
• post_attention_layernorm and post_feedforward_layernorm (post-norm pattern, no input_layernorm)
• Top-k MoE FFN with softmax routing (OlmoE-style)
• No sliding-window attention

Files

| Quant | Size | Notes |

|---|---|---|

| BAR-5x7B.Q4_K_M.gguf | 14 GB | recommended, fits 16GB VRAM at small context |

| (more quants pending) | | |

Usage

./build/bin/llama-completion \
  -m BAR-5x7B.Q4_K_M.gguf \
  -p "The 5 experts in BAR-5x7B are " \
  -n 100

Validation

The Q4_K_M conversion was validated against the patched llama.cpp build using a basic arithmetic prompt and produces correct, coherent output.

Credit

• Model: AllenAI — allenai/BAR-5x7B
• FlexOlmo support in llama.cpp: PR by @Seraphiel102 / Nyx
• Conversion: llama.cpp + the convert_hf_to_gguf.py patch from the support PR

If this saved you time, please ⭐ the llama.cpp PR.