Instructions to use summerMC/TRM-textV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use summerMC/TRM-textV2 with Transformers:

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

pipe = pipeline("text-generation", model="summerMC/TRM-textV2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("summerMC/TRM-textV2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use summerMC/TRM-textV2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "summerMC/TRM-textV2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "summerMC/TRM-textV2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/summerMC/TRM-textV2

SGLang

How to use summerMC/TRM-textV2 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 "summerMC/TRM-textV2" \
    --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": "summerMC/TRM-textV2",
		"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 "summerMC/TRM-textV2" \
        --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": "summerMC/TRM-textV2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use summerMC/TRM-textV2 with Docker Model Runner:
```
docker model run hf.co/summerMC/TRM-textV2
```

TRM-textV2 / README.md

summerMC

v36: Enhanced README and permanent chat_template integration

dc244cf verified about 1 month ago

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	---
	language: en
	license: mit
	base_model: summerMC/TRM-textV2
	tags:
	- text-generation
	- trm-text
	- ism
	- recurrent-transformer
	- tiny-stories
	library_name: transformers
	metrics:
	- accuracy
	---

	# 🤖 TRM-textV2: Recurrent Shared Transformer

	TRM-textV2 is a high-efficiency language model featuring a Shared Recurrent Transformer architecture enhanced with Inverse Square Mask (ISM) logic.

	## 🌟 Model Highlights
	- Efficient Depth: Simulates a deep network by repeating a single Transformer block (recurrence_steps=4).
	- ISM Integration: Advanced prefix-answer masking for superior long-range dependency handling.
	- Optimized for Stability: Trained with specific residual scaling and gate initialization to prevent loss plateaus.

	## 🚀 Quick Start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('summerMC/TRM-textV2', trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained('summerMC/TRM-textV2', trust_remote_code=True)

	# Standard Chat Template use
	messages = [{'role': 'user', 'content': 'Once upon a time, a small robot'}]
	inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt')
	```

	## 📊 Training Details
	- Dataset: TinyStories & FineWeb-Edu
	- Architecture: 45M parameters (Effective depth equivalent to larger models)
	- License: MIT