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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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
BAR-5x7B β 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 Γ 7B β ~33B total parameters with top-k routing.
β Requires a FlexOlmo-enabled llama.cpp
Upstream llama.cpp does not support the FlexOlmo architecture: the support PR
ggml-org/llama.cpp#22540 was closed unmerged on 2026-04-30.
Use hob-llama.cpp, our llama.cpp build that adds FlexOlmo on a current upstream base:
- Prebuilt binaries: release
hob-b11434: Linux (CPU x64/arm64, CUDA 12.8, ROCm, Vulkan), Windows (CPU x64/arm64, CUDA 12.4/13.4, ROCm, Vulkan) and macOS (Intel x64), withSHA256SUMS.txt. - Or build from source:
git clone https://github.com/Seraphiel102/llama.cpp.git
cd llama.cpp
git checkout hob-b11434
cmake -B build -DGGML_CUDA=OFF # or -DGGML_CUDA=ON etc. for your GPU
cmake --build build -j --target llama-cli llama-completion llama-server
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_layernormandpost_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 April FlexOlmo build using a basic arithmetic prompt and produces correct, coherent output. BAR-5x7B itself has not yet been re-run on b11434; the FlexOlmo graph there was verified equivalent to the April build (a tiny random FlexOlmo model, plus BAR-2x7B Q4_K_M with byte-identical output).
Credit
- Model: AllenAI β
allenai/BAR-5x7B - FlexOlmo support in llama.cpp: hob-llama.cpp by @Seraphiel102 / Nyx (upstream PR #22540 was closed)
- Conversion: llama.cpp + our FlexOlmo
convert_hf_to_gguf.pypatch
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Model tree for Hob-forge/BAR-5x7B-GGUF
Base model
allenai/BAR-5x7B