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 / results /processed /benchmark_summary.csv

Premchandyadav369

feat: complete scientific repair, canonical checkpoint training, tokenizer, GQA zero-copy, and release gating

d279df0 17 days ago

Raw History Blame Contribute Delete

2.15 kB

metric_group,variant,metric_name,value,unit,classification
cold_start_and_edge,DenseBaseline_FP32,checkpoint_size_mb,22.086,MB,MEASURED
cold_start_and_edge,DenseBaseline_FP32,total_cold_start_to_first_token_ms_mean,238.693,ms,MEASURED
cold_start_and_edge,DenseBaseline_FP32,steady_state_tpot_ms_mean,13.184,ms/token,MEASURED
cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,checkpoint_size_mb,15.32,MB,MEASURED
cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,total_cold_start_to_first_token_ms_mean,337.758,ms,MEASURED
cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,steady_state_tpot_ms_mean,45.854,ms/token,MEASURED
cold_start_and_edge,QTensorFormer_Edge_r2_FP32,checkpoint_size_mb,14.553,MB,MEASURED
cold_start_and_edge,QTensorFormer_Edge_r2_FP32,total_cold_start_to_first_token_ms_mean,364.231,ms,MEASURED
cold_start_and_edge,QTensorFormer_Edge_r2_FP32,steady_state_tpot_ms_mean,43.157,ms/token,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,checkpoint_size_mb,22.353,MB,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,total_cold_start_to_first_token_ms_mean,291.679,ms,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,steady_state_tpot_ms_mean,16.608,ms/token,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,checkpoint_size_mb,11.189,MB,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,total_cold_start_to_first_token_ms_mean,505.744,ms,MEASURED
cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,steady_state_tpot_ms_mean,32.095,ms/token,MEASURED
tt_compression,rank_1,compression_ratio_vs_dense,56.96,x,MEASURED
tt_compression,rank_1,decode_B1_T1_ms_mean,0.8833,ms,MEASURED
tt_compression,rank_2,compression_ratio_vs_dense,30.14,x,MEASURED
tt_compression,rank_2,decode_B1_T1_ms_mean,0.9745,ms,MEASURED
tt_compression,rank_4,compression_ratio_vs_dense,15.51,x,MEASURED
tt_compression,rank_4,decode_B1_T1_ms_mean,1.0353,ms,MEASURED
tt_compression,rank_8,compression_ratio_vs_dense,7.86,x,MEASURED
tt_compression,rank_8,decode_B1_T1_ms_mean,1.1157,ms,MEASURED
tt_compression,rank_16,compression_ratio_vs_dense,3.95,x,MEASURED
tt_compression,rank_16,decode_B1_T1_ms_mean,1.498,ms,MEASURED

	metric_group,variant,metric_name,value,unit,classification
	cold_start_and_edge,DenseBaseline_FP32,checkpoint_size_mb,22.086,MB,MEASURED
	cold_start_and_edge,DenseBaseline_FP32,total_cold_start_to_first_token_ms_mean,238.693,ms,MEASURED
	cold_start_and_edge,DenseBaseline_FP32,steady_state_tpot_ms_mean,13.184,ms/token,MEASURED
	cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,checkpoint_size_mb,15.32,MB,MEASURED
	cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,total_cold_start_to_first_token_ms_mean,337.758,ms,MEASURED
	cold_start_and_edge,QTensorFormer_Balanced_r8_FP32,steady_state_tpot_ms_mean,45.854,ms/token,MEASURED
	cold_start_and_edge,QTensorFormer_Edge_r2_FP32,checkpoint_size_mb,14.553,MB,MEASURED
	cold_start_and_edge,QTensorFormer_Edge_r2_FP32,total_cold_start_to_first_token_ms_mean,364.231,ms,MEASURED
	cold_start_and_edge,QTensorFormer_Edge_r2_FP32,steady_state_tpot_ms_mean,43.157,ms/token,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,checkpoint_size_mb,22.353,MB,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,total_cold_start_to_first_token_ms_mean,291.679,ms,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP32,steady_state_tpot_ms_mean,16.608,ms/token,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,checkpoint_size_mb,11.189,MB,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,total_cold_start_to_first_token_ms_mean,505.744,ms,MEASURED
	cold_start_and_edge,HF_QTensorFormer_GQA_r8_FP16,steady_state_tpot_ms_mean,32.095,ms/token,MEASURED
	tt_compression,rank_1,compression_ratio_vs_dense,56.96,x,MEASURED
	tt_compression,rank_1,decode_B1_T1_ms_mean,0.8833,ms,MEASURED
	tt_compression,rank_2,compression_ratio_vs_dense,30.14,x,MEASURED
	tt_compression,rank_2,decode_B1_T1_ms_mean,0.9745,ms,MEASURED
	tt_compression,rank_4,compression_ratio_vs_dense,15.51,x,MEASURED
	tt_compression,rank_4,decode_B1_T1_ms_mean,1.0353,ms,MEASURED
	tt_compression,rank_8,compression_ratio_vs_dense,7.86,x,MEASURED
	tt_compression,rank_8,decode_B1_T1_ms_mean,1.1157,ms,MEASURED
	tt_compression,rank_16,compression_ratio_vs_dense,3.95,x,MEASURED
	tt_compression,rank_16,decode_B1_T1_ms_mean,1.498,ms,MEASURED