Instructions to use UltranetCommand/Qwen2.5-3B-FrozenG-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UltranetCommand/Qwen2.5-3B-FrozenG-Adapter with Transformers:

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

pipe = pipeline("text-generation", model="UltranetCommand/Qwen2.5-3B-FrozenG-Adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UltranetCommand/Qwen2.5-3B-FrozenG-Adapter")
model = AutoModelForCausalLM.from_pretrained("UltranetCommand/Qwen2.5-3B-FrozenG-Adapter", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UltranetCommand/Qwen2.5-3B-FrozenG-Adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UltranetCommand/Qwen2.5-3B-FrozenG-Adapter

SGLang

How to use UltranetCommand/Qwen2.5-3B-FrozenG-Adapter 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 "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UltranetCommand/Qwen2.5-3B-FrozenG-Adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UltranetCommand/Qwen2.5-3B-FrozenG-Adapter with Docker Model Runner:
```
docker model run hf.co/UltranetCommand/Qwen2.5-3B-FrozenG-Adapter
```

Qwen2.5-3B Frozen-G Adapter (Crimson OS)

Verified Phase 2 Benchmark Receipts

Patched Attention Blocks: 36 / 36 Self-Attention Blocks
Target Base Model: Qwen/Qwen2.5-3B-Instruct
Inference Power Draw: 70.0 W (Strict Sub-TDP Cap on NVIDIA T4)
Energy Efficiency: 4.22104 J/tok
Throughput / Latency: ~60.3 ms/tok

Architectural Overview

This adapter integrates a frozen positive-definite metric tensor $G \in [0.1, 1.0]$ across all transformer self-attention query-key projections. By enforcing an invariant geometric manifold during continuous token generation, it bounds non-equilibrium steady-state (NESS) entropy drift, eliminating parameter thrashing and stabilizing long-context energy draw.

Downloads last month: 287

Safetensors

Model size

3B params

Tensor type

F16

Model tree for UltranetCommand/Qwen2.5-3B-FrozenG-Adapter

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1530)

this model