Mungert/AceInstruct-1.5B-GGUF

AceInstruct-1.5B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit b9c3eefd.

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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. These same datasets are also used in the training of AceMath-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 and paper.

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, AceMath-7B-Instruct, AceMath-72B-Instruct

AceMath Reward Models

• AceMath-7B-RM, AceMath-72B-RM

Evaluation & Training Data

• AceMath-RewardBench, AceMath-Instruct Training Data, AceMath-RM Training Data

General Instruction Models

• AceInstruct-1.5B, AceInstruct-7B, AceInstruct-72B

How to use

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, we’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 of the data generated by OpenAI. We put the AceInstruct models under the license of Creative Commons Attribution: Non-Commercial 4.0 International.

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

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. You will also find the code I use to quantize the models if you want to do it yourself 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 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. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. 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! 😊