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 /raw /frontier_research_benchmarks.json

Premchandyadav369

feat: complete 6 frontier research extensions, empirical benchmarks, and 101-test suite

b5cb5a0 26 days ago

Raw History Blame Contribute Delete

3.18 kB

{
  "status": "FRONTIER_RESEARCH_BENCHMARKS_COMPLETE",
  "timestamp_utc": "2026-10-19T03:12:08.632830+00:00",
  "benchmarks": {
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      "draft_rank": 1,
      "target_rank": 8,
      "lookahead_k": 3,
      "standard_autoregressive_tok_per_sec": 178.05970566826537,
      "speculative_tok_per_sec": 246.28039646095064,
      "speedup_ratio": 1.3831337951315275,
      "acceptance_rate": 1.0,
      "extra_vram_overhead_mb": 0.0,
      "scientific_classification": "MEASURED"
    },
    "semantic_anchor_kv_cache": {
      "sequence_length": 512,
      "cache_capacity": 128,
      "total_needles": 6,
      "retained_needles": 3,
      "retention_rate": 0.5,
      "full_fp16_memory_bytes": 262144,
      "cached_int8_memory_bytes": 32768,
      "memory_saved_pct": 87.5,
      "scientific_classification": "MEASURED"
    },
    "fused_tt_engine": {
      "in_features": 512,
      "out_features": 2048,
      "rank": 8,
      "unfused_memory_traffic_bytes": 21642240,
      "fused_memory_traffic_bytes": 670720,
      "memory_traffic_reduction_pct": 96.9008753252898,
      "unfused_arithmetic_intensity_flops_per_byte": 0.0227111426543648,
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      "fused_advantage_multiplier": 32.26717557251908,
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    },
    "contextual_bandit_allocator": {
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      "avg_bandit_reward": -0.028453125,
      "avg_static_reward": -0.1375,
      "reward_improvement_pct": 79.30681818181819,
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      "scientific_classification": "SIMULATED"
    }
  }
}

	{
	"status": "FRONTIER_RESEARCH_BENCHMARKS_COMPLETE",
	"timestamp_utc": "2026-10-19T03:12:08.632830+00:00",
	"benchmarks": {
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	"speedup_ratio": 1.3831337951315275,
	"acceptance_rate": 1.0,
	"extra_vram_overhead_mb": 0.0,
	"scientific_classification": "MEASURED"
	},
	"semantic_anchor_kv_cache": {
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	"cache_capacity": 128,
	"total_needles": 6,
	"retained_needles": 3,
	"retention_rate": 0.5,
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	"cached_int8_memory_bytes": 32768,
	"memory_saved_pct": 87.5,
	"scientific_classification": "MEASURED"
	},
	"fused_tt_engine": {
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	"rank": 8,
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