--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation tags: - nvidia - AceInstruct - code - math - general_domain - instruct_model - pytorch --- # AceInstruct-1.5B GGUF Models ## Model Generation Details This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`b9c3eefd`](https://github.com/ggerganov/llama.cpp/commit/b9c3eefde1b67104bd993485ff38dd62abe9d70c). --- Click here to get info on choosing the right GGUF model format --- ## Introduction We introduce AceInstruct, a family of advanced SFT models for coding, mathematics, and general-purpose tasks. The AceInstruct family, which includes AceInstruct-1.5B, 7B, and 72B, is Improved using Qwen. These models are fine-tuned on Qwen2.5-Base using [general SFT datasets](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data). These same datasets are also used in the training of [AceMath-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct). Different from AceMath-Instruct which is specialized for math questions, AceInstruct is versatile and can be applied to a wide range of domains. Benchmark evaluations across coding, mathematics, and general knowledge tasks demonstrate that AceInstruct delivers performance comparable to Qwen2.5-Instruct. For more information about AceInstruct, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084). ## Benchmark Results | | Qwen2.5-1.5B-Instruct | AceInstruct-1.5B | Qwen2.5-7B-Instruct | AceInstruct-7B | Qwen2.5-72B-Instruct | AceInstruct-72B | | --------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:| | HumanEval | 61.60 | 73.17 | 84.80 | 85.37 | 86.60 | 89.63 | | MBPP | 63.20 | 65.76 | 79.20 | 74.32 | 88.20 | 83.66 | | GSM8K | 73.20 | 80.44 | 91.60 | 93.10 | 95.80 | 96.36 | | MATH | 55.20 | 60.34 | 75.50 | 76.40 | 83.10 | 84.50 | | MMLU | 58.37 | 58.17 | 74.51 | 74.68 | 84.67 | 83.88 | | MMLU Pro | 32.40 | 33.78 | 56.30 | 54.50 | 71.10 | 66.10 | | Average | 57.33 | 61.94 | 76.99 | 76.40 | 84.91 | 84.02 | We compare AceInstruct to Qwen2.5-Instruct across coding, mathematics, and general knowledge tasks. We find that AceInstruct-1.5B outperforms Qwen2.5-1.5B-Instruct (61.94 vs. 57.33), while AceInstruct-7B and AceInstruct-72B perform similarly to Qwen2.5-7B-Instruct and Qwen2.5-72B-Instruct. ## All Resources ### AceMath Instruction Models - [AceMath-1.5B-Instruct](https://huggingface.co/nvidia/AceMath-1.5B-Instruct), [AceMath-7B-Instruct](https://huggingface.co/nvidia/AceMath-7B-Instruct), [AceMath-72B-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct) ### AceMath Reward Models - [AceMath-7B-RM](https://huggingface.co/nvidia/AceMath-7B-RM), [AceMath-72B-RM](https://huggingface.co/nvidia/AceMath-72B-RM) ### Evaluation & Training Data - [AceMath-RewardBench](https://huggingface.co/datasets/nvidia/AceMath-RewardBench), [AceMath-Instruct Training Data](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data), [AceMath-RM Training Data](https://huggingface.co/datasets/nvidia/AceMath-RM-Training-Data) ### General Instruction Models - [AceInstruct-1.5B](https://huggingface.co/nvidia/AceInstruct-1.5B), [AceInstruct-7B](https://huggingface.co/nvidia/AceInstruct-7B), [AceInstruct-72B](https://huggingface.co/nvidia/AceInstruct-72B) ## How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "AceInstruct-1.5B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") prompt = "Tell me something about artificial intelligence." messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to("cuda") generated_ids = model.generate( **model_inputs, max_new_tokens=1024 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Correspondence to Zihan Liu (zihanl@nvidia.com), Yang Chen (yachen@nvidia.com), Wei Ping (wping@nvidia.com) ## Citation If you find our work helpful, weโ€™d appreciate it if you could cite us. 
@article{acemath2024,
  title={AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling},
  author={Liu, Zihan and Chen, Yang and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
  journal={arXiv preprint},
  year={2024}
}
## License All models in the AceInstruct family are for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/row-terms-of-use/) of the data generated by OpenAI. We put the AceInstruct models under the license of [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0). --- # ๐Ÿš€ If you find these models useful Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: ๐Ÿ‘‰ [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) ๐Ÿ’ฌ **How to test**: Choose an **AI assistant type**: - `TurboLLM` (GPT-4.1-mini) - `HugLLM` (Hugginface Open-source models) - `TestLLM` (Experimental CPU-only) ### **What Iโ€™m Testing** Iโ€™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** ๐ŸŸก **TestLLM** โ€“ Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): - โœ… **Zero-configuration setup** - โณ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. - ๐Ÿ”ง **Help wanted!** If youโ€™re into **edge-device AI**, letโ€™s collaborate! ### **Other Assistants** ๐ŸŸข **TurboLLM** โ€“ 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) ๐Ÿ”ต **HugLLM** โ€“ Latest Open-source models: - ๐ŸŒ Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. ### ๐Ÿ’ก **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 OpenAIโ€”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) โ˜•. 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! ๐Ÿ˜Š