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Model: Mungert/Olmo-3-7B-Think-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: allenai/Olmo-3-7B-Think-DPO
+language:
+- en
+library_name: transformers
+---
+
+# <span style="color: #7FFF7F;">Olmo-3-7B-Think GGUF Models</span>
+
+
+## <span style="color: #7F7FFF;">Model Generation Details</span>
+
+This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`96ac5a232`](https://github.com/ggerganov/llama.cpp/commit/96ac5a2329029dfc35c9cbbb24c09fd91ae9416b).
+
+
+
+
+
+---
+
+## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>
+
+I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.
+
+In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:  
+👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)
+
+While this does increase model file size, it significantly improves precision for a given quantization level.
+
+### **I'd love your feedback—have you tried this? How does it perform for you?**
+
+
+
+
+---
+
+<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
+  Click here to get info on choosing the right GGUF model format
+</a>
+
+---
+
+
+
+<!--Begin Original Model Card-->
+
+
+## Model Details
+<img alt="OLMo Logo" src="https://cdn-uploads.huggingface.co/production/uploads/65316953791d5a2611426c20/nC44-uxMD6J6H3OHxRtVU.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">
+
+
+# Model Card for Olmo 3 Think
+
+We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.
+
+Olmo is a series of **O**pen **l**anguage **mo**dels designed to enable the science of language models. 
+These models are pre-trained on the Dolma 3 dataset and post-trained on the Dolci datasets. We are releasing all code, checkpoints, logs (coming soon), and associated training details. 
+
+
+
+The core models released in this batch include the following:
+
+| **Stage**               | **Olmo 3 7B Think** | **Olmo 3 32B Think** | **Olmo 3 7B Instruct** |
+|--------------------------|-----------------------|------------------------|---------------------------|
+| **Base Model**           | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | [Olmo-3-32B](https://huggingface.co/allenai/Olmo-3-1125-32B) | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) |
+| **SFT**                  | [Olmo-3-7B-Think-SFT](https://huggingface.co/allenai/Olmo-3-7B-Think-SFT) | [Olmo-3-32B-Think-SFT](https://huggingface.co/allenai/Olmo-3-32B-Think-SFT) | [Olmo-3-7B-Instruct-SFT](https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT) |
+| **DPO**                  | [Olmo-3-7B-Think-DPO](https://huggingface.co/allenai/Olmo-3-7B-Think-DPO) | [Olmo-3-32B-Think-DPO](https://huggingface.co/allenai/Olmo-3-32B-Think-DPO) | [Olmo-3-7B-Instruct-DPO](https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO) |
+| **Final Models (RLVR)**  | [Olmo-3-7B-Think](https://huggingface.co/allenai/Olmo-3-7B-Think) | [Olmo-3-32B-Think](https://huggingface.co/allenai/Olmo-3-32B-Think) | [Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-Instruct) |            
+             
+
+## Installation
+
+Olmo 3 is supported in transformers 4.57.0 or higher:
+```bash
+pip install transformers>=4.57.0
+```
+
+## Inference
+
+You can use OLMo with the standard HuggingFace transformers library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think")
+tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")
+message = ["Who would win in a fight - a dinosaur or a cow named Moo Moo?"]
+inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
+# optional verifying cuda
+# inputs = {k: v.to('cuda') for k,v in inputs.items()}
+# olmo = olmo.to('cuda')
+response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
+print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
+>> '<think>Okay, so the question is who would win in a fight...'
+```
+
+For faster performance, you can quantize the model using the following method:
+```python
+AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", 
+    torch_dtype=torch.float16, 
+    load_in_8bit=True)  # Requires bitsandbytes
+```
+The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:
+```python
+inputs.input_ids.to('cuda')
+```
+
+We have released checkpoints for these models. For post-training, the naming convention is `step_XXXX`. 
+
+
+To load a specific model revision with HuggingFace, simply add the argument `revision`:
+```bash
+olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", revision="step_1375")
+```
+
+Or, you can access all the revisions for the models via the following code snippet:
+```python
+from huggingface_hub import list_repo_refs
+out = list_repo_refs("allenai/Olmo-3-7B-Think")
+branches = [b.name for b in out.branches]
+```
+
+### Chat template
+
+## Default System Message
+The default system prompt for this model is:
+```
+<|im_start|>system
+You are Olmo, a helpful AI assistant built by Ai2. Your date cutoff is December 2024, and your model weights are available at https://huggingface.co/allenai.
+<|im_end|>
+```
+
+## Chat Format
+
+The chat template for this model is formatted as:
+```
+<|im_start|>system
+You are Olmo, a helpful AI assistant built by Ai2. Your date cutoff is December 2024, and your model weights are available at https://huggingface.co/allenai.
+<|im_start|>user
+Who would win in a fight - a dinosaur or a cow named Moo Moo?<|im_end|>
+<|im_start|>assistant
+<think>Okay, so the question is who would win in a fight between a dinosaur and a cow named Moo Moo.
+Hmm, first I need to break this down. Let me think about the different factors involved here..... </think>
+Moo Moo the cow would certinaly win.
+<|endoftext|>
+```
+
+### 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 Think 7B SFT | Olmo 3 Think 7B DPO | Olmo 3 Think 7B | OpenThinker3-7B | Nemotron-Nano-9B-v2 | DeepSeek-R1-Distill-Qwen-7B | Qwen 3 8B (reasoning) | Qwen 3 VL 8B Thinker | OpenReasoning Nemotron 7B |
+|-------|-----------|------------------|------------------|--------------|------------------|-----------------------|------------------------------|-------------------------|---------------------------|-----------------------------|
+| **Math** | MATH | 94.4 | 92.4 | 95.1 | 94.5 | 94.4 | 87.9 | 95.1 | 95.2 | 94.6 |
+|  | AIME 2024 | 69.6 | 74.6 | 71.6 | 67.7 | 72.1 | 54.9 | 74.0 | 70.9 | 77.0 |
+|  | AIME 2025 | 57.6 | 62.7 | 64.6 | 57.2 | 58.9 | 40.2 | 67.8 | 61.5 | 73.1 |
+|  | OMEGA | 45.0 | 40.5 | 37.8 | 38.4 | 42.4 | 28.5 | 43.4 | 38.1 | 43.2 |
+| **Reasoning** | BBH | 84.1 | 83.7 | 86.6 | 77.1 | 86.2 | 73.5 | 84.4 | 86.8 | 81.3 |
+|  | ZebraLogic | 57.9 | 60.6 | 66.5 | 34.9 | 60.8 | 26.1 | 85.2 | 91.2 | 22.4 |
+|  | AGI Eval | 77.2 | 79.1 | 81.5 | 78.6 | 83.1 | 69.5 | 87.0 | 90.1 | 81.4 |
+| **Coding** | HumanEval+ | 88.2 | 91.4 | 89.9 | 87.4 | 89.7 | 83.0 | 80.2 | 83.7 | 89.7 |
+|  | MBPP+ | 63.2 | 63.0 | 64.7 | 61.4 | 66.1 | 63.5 | 69.1 | 63.0 | 61.2 |
+|  | LCB v3 | 67.8 | 75.1 | 75.2 | 68.0 | 83.4 | 58.8 | 86.2 | 85.5 | 82.3 |
+| **IF** | IFEval | 77.9 | 75.9 | 88.2 | 51.7 | 86.0 | 59.6 | 87.4 | 85.5 | 42.5 |
+|  | IFBench | 30.0 | 28.3 | 41.6 | 23.0 | 34.6 | 16.7 | 37.1 | 40.4 | 23.4 |
+| **Knowledge** | MMLU | 74.9 | 74.8 | 77.8 | 77.4 | 84.3 | 67.9 | 85.4 | 86.5 | 80.7 |
+| **QA** | PopQA | 20.8 | 24.7 | 23.7 | 18.0 | 17.9 | 12.8 | 24.3 | 29.3 | 14.5 |
+|  | GPQA | 45.8 | 48.6 | 46.2 | 47.6 | 56.2 | 54.4 | 57.7 | 61.5 | 56.6 |
+| **Chat** | AE 2 | 43.9 | 50.6 | 52.1 | 24.0 | 58.0 | 7.7 | 60.5 | 73.5 | 8.6 |
+| **Safety** |  | 65.8 | 67.7 | 70.7 | 31.3 | 72.1 | 54.0 | 68.3 | 82.9 | 30.3 |
+
+## 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`
+
+### transformers Example
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "allenai/Olmo-3-7B-Think"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+)
+
+prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    temperature=0.6,
+    top_p=0.95,
+    max_new_tokens=32768,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### vllm Example
+```python
+from vllm import LLM, SamplingParams
+
+model_id = "allenai/Olmo-3-7B-Think"
+llm = LLM(model=model_id)
+
+sampling_params = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=32768,
+)
+
+prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
+outputs = llm.generate(prompt, sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+
+## Bias, Risks, and Limitations
+Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.
+
+
+## Citation
+A technical manuscript is forthcoming!
+
+## Model Card Contact
+For errors in this model card, contact `olmo@allenai.org`.
+
+<!--End Original Model Card-->
+
+---
+
+# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
+
+Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  
+
+👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  
+
+
+The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)
+
+💬 **How to test**:  
+ Choose an **AI assistant type**:  
+   - `TurboLLM` (GPT-4.1-mini)  
+   - `HugLLM` (Hugginface Open-source models)  
+   - `TestLLM` (Experimental CPU-only)  
+
+### **What I’m Testing**  
+I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
+- **Function calling** against live network services  
+- **How small can a model go** while still handling:  
+  - Automated **Nmap security scans**  
+  - **Quantum-readiness checks**  
+  - **Network Monitoring tasks**  
+
+🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  
+- ✅ **Zero-configuration setup**  
+- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
+- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  
+
+### **Other Assistants**  
+🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
+- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. 
+- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
+- **Real-time network diagnostics and monitoring**
+- **Security Audits**
+- **Penetration testing** (Nmap/Metasploit)  
+
+🔵 **HugLLM** – Latest Open-source models:  
+- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
+
+### 💡 **Example commands you could test**:  
+1. `"Give me info on my websites SSL certificate"`  
+2. `"Check if my server is using quantum safe encyption for communication"`  
+3. `"Run a comprehensive security audit on my server"`
+4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!
+
+### Final Word
+
+I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.
+
+If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
+
+I'm also open to job opportunities or sponsorship.
+
+Thank you! 😊

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "others", "allow_remote": true}