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
| 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. | |