Mungert/Llama-3.1-Minitron-4B-Depth-Base-GGUF

Llama-3.1-Minitron-4B-Depth-Base GGUF Models

Model Generation Details

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

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Quantization Beyond the IMatrix

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

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?

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Click here to get info on choosing the right GGUF model format

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Llama-3.1-Minitron-4B-Depth-Base

Model Overview

Llama-3.1-Minitron-4B-Depth-Base is a base text-to-text model that can be adopted for a variety of natural language generation tasks. It is obtained by pruning Llama-3.1-8B; specifically, we prune the number of transformer blocks in the model. Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose. Please refer to our technical report for more details.

This model is ready for commercial use.

Model Developer: NVIDIA

Model Dates: Llama-3.1-Minitron-4B-Depth-Base was trained between July 29, 2024 and Aug 3, 2024

License

This model is released under the NVIDIA Open Model License Agreement.

Model Architecture

Llama-3.1-Minitron-4B-Depth-Base uses a model embedding size of 4096, 32 attention heads, MLP intermediate dimension of 14336, with 32 layers in total. Additionally, it uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).

Architecture Type: Transformer Decoder (Auto-Regressive Language Model)

Network Architecture: Llama-3.1

Input Type(s): Text

Input Format(s): String

Input Parameters: None

Other Properties Related to Input: Works well within 8k characters or less.

Output Type(s): Text

Output Format: String

Output Parameters: 1D

Other Properties Related to Output: None

Usage

import torch
from transformers import AutoTokenizer, LlamaForCausalLM

# Load the tokenizer and model
model_path = "nvidia/Llama-3.1-Minitron-4B-Depth-Base"
tokenizer = AutoTokenizer.from_pretrained(model_path)

device = 'cuda'
dtype = torch.bfloat16
model = LlamaForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)

# Prepare the input text
prompt = 'Complete the paragraph: our solar system is'
inputs = tokenizer.encode(prompt, return_tensors='pt').to(model.device)

# Generate the output
outputs = model.generate(inputs, max_length=20)

# Decode and print the output
output_text = tokenizer.decode(outputs[0])
print(output_text)

Software Integration

Runtime Engine(s):

• NeMo 24.05

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Hopper
• NVIDIA Lovelace

[Preferred/Supported] Operating System(s):

• Linux

Dataset & Training

Data Collection Method by Dataset: Automated

Labeling Method by Dataset: Not Applicable

Properties: The training corpus for Llama-3.1-Minitron-4B-Depth-Base consists of English and multilingual text, as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. In our continued training set, we introduce a small portion of question-answering, and alignment style data to improve model performance.

Data Freshness: The pretraining data has a cutoff of June 2023.

Evaluation Results

Overview

5-shot performance. Language Understanding evaluated using Massive Multitask Language Understanding:

Average
58.7

Zero-shot performance. Evaluated using select datasets from the LM Evaluation Harness with additions:

HellaSwag	Winogrande	GSM8K	ARC-Challenge	XLSum
73.2	72.1	16.8	52.6	27.2

Code generation performance. Evaluated using MBPP:

Score
30.7

Inference

Engine: TensorRT-LLM

Test Hardware: NVIDIA A100

DType: BFloat16

Limitations

The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

References

• Compact Language Models via Pruning and Knowledge Distillation
• LLM Pruning and Distillation in Practice: The Minitron Approach

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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! 😊