Instructions to use firmanda/Olmo-3-7B-Think-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 firmanda/Olmo-3-7B-Think-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 firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf firmanda/Olmo-3-7B-Think-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 firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf firmanda/Olmo-3-7B-Think-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 firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf firmanda/Olmo-3-7B-Think-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 firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M
Use Docker
docker model run hf.co/firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use firmanda/Olmo-3-7B-Think-GGUF with Ollama:
ollama run hf.co/firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use firmanda/Olmo-3-7B-Think-GGUF with Docker Model Runner:
docker model run hf.co/firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M
- Lemonade
How to use firmanda/Olmo-3-7B-Think-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull firmanda/Olmo-3-7B-Think-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Olmo-3-7B-Think-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 4,544 Bytes
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license: apache-2.0
language:
- en
base_model:
- allenai/Olmo-3-7B-Think
---
Static quant of https://huggingface.co/allenai/Olmo-3-7B-Instruct
### Model Description
- **Developed by:** Allen Institute for AI (Ai2)
- **Model type:** a Transformer style autoregressive language model.
- **Language(s) (NLP):** English
- **License:** This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).
- **Contact:** Technical inquiries: `olmo@allenai.org`. Press: `press@allenai.org`
- **Date cutoff:** Dec. 2024.
### Model Sources
- **Project Page:** https://allenai.org/olmo
- **Repositories:**
- Open-Instruct for DPO and RLVR: https://github.com/allenai/open-instruct
- OLMo-Core for pre-training and SFT: https://github.com/allenai/OLMo-core
- OLMo-Eval for evaluation: https://github.com/allenai/OLMo-Eval
- **Paper:** [TBD]
<!-- - **Technical blog post:** (URL) -->
<!-- - **W&B Logs:** [SFT](()), [DPO](()), [RLVR](()) -->
## Evaluation
| **Skill** | **Benchmark** | **Olmo 3 Instruct 7B SFT** | **Olmo 3 Instruct 7B DPO** | **Olmo3 Instruct 7B** | **Qwen 3 8B (no reasoning)** | **Qwen 3 VL 8B Instruct** | **Qwen 2.5 7B** | **Olmo 2 7B Instruct** | **Apertus 8B Instruct** | **Granite 3.3 8B Instruct** |
|-----------|--------------|---------------------------|---------------------------|------------------------|------------------------------|----------------------------|-------------------|--------------------------|----------------------------|-------------------------------|
| **Math** | MATH | 65.1 | 79.6 | 87.3 | 82.3 | 91.6 | 71.0 | 30.1 | 21.9 | 67.3 |
| | AIME 2024 | 6.7 | 23.5 | 44.3 | 26.2 | 55.1 | 11.3 | 1.3 | 0.5 | 7.3 |
| | AIME 2025 | 7.2 | 20.4 | 32.5 | 21.7 | 43.3 | 6.3 | 0.4 | 0.2 | 6.3 |
| | OMEGA | 14.4 | 22.8 | 28.9 | 20.5 | 32.3 | 13.7 | 5.2 | 5.0 | 10.7 |
| **Reasoning** | BigBenchHard | 51.0 | 69.3 | 71.2 | 73.7 | 85.6 | 68.8 | 43.8 | 42.2 | 61.2 |
| | ZebraLogic | 18.0 | 28.4 | 32.9 | 25.4 | 64.3 | 10.7 | 5.3 | 5.3 | 17.6 |
| | AGI Eval English | 59.2 | 64.0 | 64.4 | 76.0 | 84.5 | 69.8 | 56.1 | 50.8 | 64.0 |
| **Coding** | HumanEvalPlus | 69.8 | 72.9 | 77.2 | 79.8 | 82.9 | 74.9 | 25.8 | 34.4 | 64.0 |
| | MBPP+ | 56.5 | 55.9 | 60.2 | 64.4 | 66.3 | 62.6 | 40.7 | 42.1 | 54.0 |
| | LiveCodeBench v3 | 20.0 | 18.8 | 29.5 | 53.2 | 55.9 | 34.5 | 7.2 | 7.8 | 11.5 |
| **IF** | IFEval | 81.7 | 82.0 | 85.6 | 86.3 | 87.8 | 73.4 | 72.2 | 71.4 | 77.5 |
| | IFBench | 27.4 | 29.3 | 32.3 | 29.3 | 34.0 | 28.4 | 26.7 | 22.1 | 22.3 |
| **Knowledge** | MMLU | 67.1 | 69.1 | 69.1 | 80.4 | 83.6 | 77.2 | 61.6 | 62.7 | 63.5 |
| **QA** | PopQA | 16.5 | 20.7 | 14.1 | 20.4 | 26.5 | 21.5 | 25.5 | 25.5 | 28.9 |
| | GPQA | 30.0 | 37.9 | 40.4 | 44.6 | 51.1 | 35.6 | 31.3 | 28.8 | 33.0 |
| **Chat** | AlpacaEval 2 LC | 21.8 | 43.3 | 40.9 | 49.8 | 73.5 | 23.0 | 18.3 | 8.1 | 28.6 |
| **Tool Use** | SimpleQA | 74.2 | 79.8 | 79.3 | 79.0 | 90.3 | 78.0 | β | β | β |
| | LitQA2 | 38.0 | 43.3 | 38.2 | 39.6 | 30.7 | 29.8 | β | β | β |
| | BFCL | 48.9 | 49.6 | 49.8 | 60.2 | 66.2 | 55.8 | β | β | β |
| **Safety** | Safety | 89.2 | 90.2 | 87.3 | 78.0 | 80.2 | 73.4 | 93.1 | 72.2 | 73.7 |
## Model Details
#### Stage 1: SFT
- supervised fine-tuning on the Dolci-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
- Datasets: [Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/dolci-thinking-sft), [Dolci-Instruct-SFT-7B](https://huggingface.co/datasets/allenai/dolci-instruct-sft)
#### Stage 2:DPO
- direct preference optimization on the Dolci-Think-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
- Datasets: [Dolci-Think-DPO-7B](https://huggingface.co/datasets/allenai/dolci-thinking-dpo), [Dolci-Instruct-DPO-7B](https://huggingface.co/datasets/allenai/dolci-3-instruct-dpo-with-metadata)
#### Stage 3: RLVR
- reinforcement learning from verifiable rewards on the Dolci-Think-RL-7B dataset. This dataset consits of math, code, instruction-following, and general chat queries.
- Datasets: [Dolci-Think-RL-7B](https://huggingface.co/datasets/allenai/Dolci-Think-RL-7B), [Dolci-Instruct-RL-7B](https://huggingface.co/datasets/allenai/Dolci-Instruct-RL-7B)
## Inference & Recommended Settings
We evaluated our models on the following settings. We also recommend using them for generation:
- **temperature:** `0.6`
- **top_p:** `0.95`
- **max_tokens:** `32768` |