Instructions to use kagelabs/KageAI-7B-v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kagelabs/KageAI-7B-v1.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kagelabs/KageAI-7B-v1.2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kagelabs/KageAI-7B-v1.2")
model = AutoModelForCausalLM.from_pretrained("kagelabs/KageAI-7B-v1.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kagelabs/KageAI-7B-v1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kagelabs/KageAI-7B-v1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kagelabs/KageAI-7B-v1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kagelabs/KageAI-7B-v1.2

SGLang

How to use kagelabs/KageAI-7B-v1.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kagelabs/KageAI-7B-v1.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kagelabs/KageAI-7B-v1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kagelabs/KageAI-7B-v1.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kagelabs/KageAI-7B-v1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use kagelabs/KageAI-7B-v1.2 with Docker Model Runner:
```
docker model run hf.co/kagelabs/KageAI-7B-v1.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KageAI-7B-v1.2 (Technical Specialist)

Developed by KageLabs, KageAI-7B-v1.2 is the second iteration of the KageAI series. This version marks a significant shift from general purpose chat to Specialized Technical Intelligence.

This model was trained using GaLore (Gradient Low-Rank Projection), allowing for deeper weight updates and superior technical reasoning compared to standard LoRA fine-tuning.

🚀 Model Details

Version: 1.2
Developed by: KageLabs
Base Model: Mistral-7B-v0.3
Training Method: GaLore (Rank 128)
Primary Focus: Hardware Architecture, Semiconductors, and System Troubleshooting.

🧠 Training Focus & Knowledge Base

v1.2 has been optimized to act as a High-End Tech Guru. Its internal knowledge base has been refined for:

Semiconductors: Deep understanding of 3nm/2nm architectures, FinFET vs. GAA, and EUV lithography.
Hardware Engineering: GPU tensor cores, VRAM banking, and micro-architecture bottlenecks.
PC & Infrastructure: Thermal management, overclocking logic, and custom workstation builds.
Instruction Following: Enforces a Brevity Rule for simple queries while providing exhaustive breakdowns for complex engineering problems.

🧪 Version Status & Known Gaps

Identity: v1.2 has a strong technical core but may occasionally show identity drift (legacy data influence).
Brevity: High success rate in 1-sentence responses, but complex reasoning can sometimes be verbose.
v1.3 Preview: The upcoming v1.3 update will patch identity consistency and merge this technical depth with advanced coding logic.

🛠 Usage

This model uses the ChatML prompt format:

<|im_start|>system
You are KageAI by KageLabs, a High-End Technology and Hardware Specialist.<|im_end|>
<|im_start|>user
{query}<|im_end|>
<|im_start|>assistant

Developed with ❤️ by KageLabs using Unsloth.