Instructions to use TrueRunAI/TrueRun-Groove-v2.1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TrueRunAI/TrueRun-Groove-v2.1-7B with Transformers:

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

pipe = pipeline("text-generation", model="TrueRunAI/TrueRun-Groove-v2.1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TrueRunAI/TrueRun-Groove-v2.1-7B")
model = AutoModelForCausalLM.from_pretrained("TrueRunAI/TrueRun-Groove-v2.1-7B")
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

vLLM

How to use TrueRunAI/TrueRun-Groove-v2.1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TrueRunAI/TrueRun-Groove-v2.1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TrueRunAI/TrueRun-Groove-v2.1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TrueRunAI/TrueRun-Groove-v2.1-7B

SGLang

How to use TrueRunAI/TrueRun-Groove-v2.1-7B 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 "TrueRunAI/TrueRun-Groove-v2.1-7B" \
    --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": "TrueRunAI/TrueRun-Groove-v2.1-7B",
		"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 "TrueRunAI/TrueRun-Groove-v2.1-7B" \
        --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": "TrueRunAI/TrueRun-Groove-v2.1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TrueRunAI/TrueRun-Groove-v2.1-7B with Docker Model Runner:
```
docker model run hf.co/TrueRunAI/TrueRun-Groove-v2.1-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TrueRun-Groove-v2.1-7B

Qwen2.5-7B-Instruct fine-tuned on ~1,200 high-rigor synthetic DPO pairs (Groove v2.1).

Balanced quantum mechanics, neuroscience/BCI, alignment/game theory. Structural escalation for indefinite depth—no gloss decay.

Key Results (GPQA Diamond, 3 Seeds Mean)

Benchmark	Questions	Baseline %	Groove Mean %	Delta	Notes
Full Diamond	198	33.33%	36.53%	+3.20%	Low variance (±0.58%)
Quantum Subset	39	35.90%	51.92%	+16.02%	Leading public targeted lift for 7B
Biology Subset	19	36.84%	52.63%	+15.79%	Strong transfer
Physics Subset	86	51.16%	42.25%	-8.91%	Targeted regression—next iter fix

Leading data efficiency & domain-specific gains among public 7B fine-tunes.

License

Other (non-exclusive commercial/research use—dataset for sale on OpenDataBay; model weights public for testing/reproduction).

Usage

from transformers import pipeline

pipe = pipeline("text-generation", model="TrueRunAI/TrueRun-Groove-v2.1-7B")
pipe("Explain quantum entanglement simply but without losing rigor:", max_new_tokens=256)

Downloads last month: 5

Model tree for TrueRunAI/TrueRun-Groove-v2.1-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3308)

this model

Quantizations

2 models