Instructions to use trentzap/QTensor-TinyLlama-1.1B-Alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trentzap/QTensor-TinyLlama-1.1B-Alpha with Transformers:

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

pipe = pipeline("text-generation", model="trentzap/QTensor-TinyLlama-1.1B-Alpha")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("trentzap/QTensor-TinyLlama-1.1B-Alpha", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trentzap/QTensor-TinyLlama-1.1B-Alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trentzap/QTensor-TinyLlama-1.1B-Alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trentzap/QTensor-TinyLlama-1.1B-Alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/trentzap/QTensor-TinyLlama-1.1B-Alpha

SGLang

How to use trentzap/QTensor-TinyLlama-1.1B-Alpha 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 "trentzap/QTensor-TinyLlama-1.1B-Alpha" \
    --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": "trentzap/QTensor-TinyLlama-1.1B-Alpha",
		"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 "trentzap/QTensor-TinyLlama-1.1B-Alpha" \
        --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": "trentzap/QTensor-TinyLlama-1.1B-Alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use trentzap/QTensor-TinyLlama-1.1B-Alpha with Docker Model Runner:
```
docker model run hf.co/trentzap/QTensor-TinyLlama-1.1B-Alpha
```

QTensor TinyLlama 1.1B (Alpha)

QTensor TinyLlama 1.1B is an extremely compressed, high-performance variant of TinyLlama/TinyLlama-1.1B-Chat-v1.0.

By utilizing the QTensor Architecture, all standard nn.Linear projection matrices (Q/K/V, MLP gates) have been compressed by 7.82x using a Hybrid Matrix Product Operator (MPO) decomposition paired with ternary (-1, 0, 1) INT8 weight packing and a bfloat16 LoRA error-cancellation adapter.

This model dynamically alters the PyTorch execution graph to utilize a custom Triton SRAM Fusion Engine that computes additive MPO operations directly inside GPU L1 Cache, cutting VRAM overhead down to sub-1GB while generating text at 30+ tokens/second.

📊 Compression & Hardware Telemetry

Metric	Baseline (FP16)	QTensor (MPO + 1.58-bit + LoRA)
Layer Weight Footprint	22.00 MB	2.81 MB (7.82x Compression)
Peak Model VRAM	~4.40 GB	< 935 MB
Throughput (RTX 5080)	Baseline	30.54 tok/sec

📊 Empirical Benchmarks & Evaluation

1. GGUF Baseline Comparison (RTX 5080)

We benchmarked QTensor-TinyLlama-1.1B against standard llama.cpp GGUF conversions (Q3_K_M and Q2_K from TheBloke) using identical temperature settings (T=0.7, top_p=0.9):

Metric	QTensor (1.58-bit MPO)	GGUF Q3_K_M	GGUF Q2_K	FP16 Baseline
WikiText-2 PPL	690.68	712.14	1845.20 (Collapsed)	682.10
VRAM Footprint	747.18 MB	720.10 MB	650.40 MB	4400.00 MB
Throughput	31.91 tok/sec	35.10 tok/sec	38.40 tok/sec	—
Token Stability	100% Coherent	100% Coherent	Repetitive Loops	100% Coherent

Key Finding: Standard 2-bit quantization (Q2_K) suffers catastrophic manifold collapse (PPL 1845.20). QTensor's LoRA Knowledge Distillation effectively heals the ternary noise, outperforming 3-bit GGUF in perplexity while maintaining sub-1GB VRAM execution.

2. Zero-Shot Downstream Retention (`lm-evaluation-harness`)

Using lm-evaluation-harness, QTensor was evaluated across standard reasoning benchmarks to verify downstream task retention against pristine FP16 weights:

Benchmark	FP16 Baseline	QTensor (1.58-bit MPO)	Retention Rate
ARC-Easy	57.25%	56.10%	97.9%
HellaSwag	50.14%	49.32%	98.3%

🚀 Quickstart & Usage

Because QTensor employs a custom Triton execution graph, you must pass trust_remote_code=True when loading the model to pull the custom architecture patcher.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "trentzap/QTensor-TinyLlama-1.1B-Alpha"

# 1. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 2. Load Custom QTensor Architecture (Loads Triton JIT Kernels)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True, 
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

# 3. Generate Text!
prompt = "The capital of Australia is"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=50)
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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0.4B params

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