Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Premchan369/Q-TensorFormer with Transformers:

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

pipe = pipeline("text-generation", model="Premchan369/Q-TensorFormer", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Premchan369/Q-TensorFormer", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Premchan369/Q-TensorFormer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Premchan369/Q-TensorFormer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Premchan369/Q-TensorFormer

SGLang

How to use Premchan369/Q-TensorFormer 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 "Premchan369/Q-TensorFormer" \
    --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": "Premchan369/Q-TensorFormer",
		"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 "Premchan369/Q-TensorFormer" \
        --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": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Premchan369/Q-TensorFormer with Docker Model Runner:
```
docker model run hf.co/Premchan369/Q-TensorFormer
```

Q-TensorFormer / src /hf_model.py

Commit History

feat(hf): turnkey custom model with TensorTrainLinear, AdaptiveKVCache, PID controller, NVML profiler, OpenAI server, and drop-in compressor

15a4381

Premchandyadav369 commited on 1 day ago

Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard

0431133

Premchandyadav369 commited on Sep 6

Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture

eaeea8f

Premchandyadav369 commited on Sep 6

Commit History

feat(hf): turnkey custom model with TensorTrainLinear, AdaptiveKVCache, PID controller, NVML profiler, OpenAI server, and drop-in compressor 15a4381

Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard 0431133

Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture eaeea8f

feat(hf): turnkey custom model with TensorTrainLinear, AdaptiveKVCache, PID controller, NVML profiler, OpenAI server, and drop-in compressor

15a4381

Implement top-tier research features: PID Dual Controller, GQA, Roofline Analyzer, Early-Exit, Meyer-Wallach Entanglement, and Interactive Visual Dashboard

0431133

Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture

eaeea8f