Text Generation
GGUF
English
allenai
bar
flex-olmo
olmo
Mixture of Experts
mixture-of-experts
llama.cpp
quantized
q4_k_m
16gb
24gb
conversational
Instructions to use Hob-forge/BAR-5x7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Hob-forge/BAR-5x7B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Hob-forge/BAR-5x7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Hob-forge/BAR-5x7B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Hob-forge/BAR-5x7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hob-forge/BAR-5x7B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hob-forge/BAR-5x7B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hob-forge/BAR-5x7B-GGUF:Q4_K_M
- Ollama
How to use Hob-forge/BAR-5x7B-GGUF with Ollama:
ollama run hf.co/Hob-forge/BAR-5x7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Hob-forge/BAR-5x7B-GGUF with Docker Model Runner:
docker model run hf.co/Hob-forge/BAR-5x7B-GGUF:Q4_K_M
- Lemonade
How to use Hob-forge/BAR-5x7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Hob-forge/BAR-5x7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.BAR-5x7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 2,439 Bytes
1b3441b 89edf04 1b3441b 89edf04 b8bd377 1b3441b 89edf04 1b3441b 89edf04 1b3441b 729c660 1b3441b 729c660 1b3441b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
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`](https://huggingface.co/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:**
```bash
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`](https://github.com/huggingface/transformers/tree/main/src/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
```bash
./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`](https://huggingface.co/allenai/BAR-5x7B)
- **FlexOlmo support in llama.cpp:** PR by [@Seraphiel102](https://github.com/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.
|