Instructions to use KHALM-Labs/aegisnode-validate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use KHALM-Labs/aegisnode-validate with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KHALM-Labs/aegisnode-validate", filename="aegisnode-validate-6.7b.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KHALM-Labs/aegisnode-validate with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KHALM-Labs/aegisnode-validate:Q4_K_M # Run inference directly in the terminal: llama-cli -hf KHALM-Labs/aegisnode-validate:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KHALM-Labs/aegisnode-validate:Q4_K_M # Run inference directly in the terminal: llama-cli -hf KHALM-Labs/aegisnode-validate: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 KHALM-Labs/aegisnode-validate:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf KHALM-Labs/aegisnode-validate: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 KHALM-Labs/aegisnode-validate:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf KHALM-Labs/aegisnode-validate:Q4_K_M
Use Docker
docker model run hf.co/KHALM-Labs/aegisnode-validate:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use KHALM-Labs/aegisnode-validate with Ollama:
ollama run hf.co/KHALM-Labs/aegisnode-validate:Q4_K_M
- Unsloth Studio new
How to use KHALM-Labs/aegisnode-validate with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KHALM-Labs/aegisnode-validate to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KHALM-Labs/aegisnode-validate to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KHALM-Labs/aegisnode-validate to start chatting
- Docker Model Runner
How to use KHALM-Labs/aegisnode-validate with Docker Model Runner:
docker model run hf.co/KHALM-Labs/aegisnode-validate:Q4_K_M
- Lemonade
How to use KHALM-Labs/aegisnode-validate with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KHALM-Labs/aegisnode-validate:Q4_K_M
Run and chat with the model
lemonade run user.aegisnode-validate-Q4_K_M
List all available models
lemonade list
tags:
- terraform
- aws
- infrastructure-as-code
- hcl
- unsloth
- deepseek-coder
- lora
license: apache-2.0
language:
- en
base_model:
- deepseek-ai/deepseek-coder-6.7b-instruct
๐ก๏ธ AegisNode Validate (6.7B)
AegisNode Validate is a specialized code-generation model fine-tuned to write syntactically flawless, zero-yapping AWS Terraform (HCL). It is built on top of deepseek-coder-6.7b-instruct using Unsloth and QLoRA.
This model is Phase 1 of a larger Curriculum Learning pipeline. It has been strictly trained to master the "grammar" of Terraform, complex referencing (depends_on, lifecycle), and strict adherence to the AWS Provider ~> 5.0 format.
๐จ CRITICAL WARNING: SYNTAX ONLY ๐จ
This model has ONLY been trained against terraform validate.
While the output will be structurally and syntactically perfect HCL, it is not guaranteed to pass terraform plan or deploy successfully. * It may hallucinate AWS region constraints (e.g., placing CloudFront WAFs outside us-east-1).
- It may create logically orphaned resources (e.g., generating a KMS key but forgetting to attach it to a database).
- It has not yet been trained on Checkov/tfsec security policies.
Do not deploy this code to production without human review. This model is intended to be used as a high-speed bootstrapping tool or a "Teacher Model" for generating training data for more advanced logic pipelines.
๐ง Model Behavior: The "Zero-Yapping" Guarantee
Unlike standard conversational LLMs, AegisNode Validate has been trained on a heavily filtered dataset to completely eliminate conversational filler.
- It will not say "Here is your code."
- It will not apologize.
- It will not output markdown wrappers (
hcl) unless explicitly prompted. - It outputs RAW, executable HCL from the very first token.
๐ป Usage (Ollama / GGUF)
Because this model relies on the native DeepSeek-Coder template, you must use the correct instruction formatting. If you download the .gguf file, use the following Modelfile to run it in Ollama:
Create and run the model:
ollama create aegisnode-validate -f Modelfile
ollama run aegisnode-validate "Create a VPC in us-east-1 with CIDR 10.0.0.0/16 and two public subnets."
๐ Training Details
- Base Model:
deepseek-ai/deepseek-coder-6.7b-instruct - Dataset: 3,470 meticulously refined and augmented Terraform trajectories.
- Hardware: 1x NVIDIA RTX 5070TI (32GB VRAM)
- Framework: Unsloth + Huggingface TRL
- Hyperparameters: Rank 8, Alpha 16, LR 2e-5, Cosine Decay, 1 Epoch. (Trained explicitly on Assistant responses only).
๐ The AegisNode Roadmap
- Phase 1 (AegisNode Validate): Master HCL syntax and formatting (
terraform validate). - Phase 2 (AegisNode Plan): Master AWS API logic and state relationships (
terraform plan). - Phase 3 (AegisNode Hiraya): Master enterprise security and compliance (
checkov/tfsec).