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, device_map="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
```

summerMC commited on Jun 7

Commit

29ecdb7

verified ·

1 Parent(s): 73aa0ce

Final Release v35: Complete model files and updated README

Browse files

Files changed (1) hide show

README.md +20 -24

README.md CHANGED Viewed

@@ -1,30 +1,26 @@
-# TRM-Text ISM
-Custom Hugging Face Transformers model.
-## Load
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-path = "/content/trm_text_ism_hf"
-tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    path,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
 ```
-## Config
-* dim: 768
-* heads: 12
-* head_dim: 64
-* recurrence_steps: 4
-* gate_style: stable
-* max_seq_len: 512

+---
+language: en
+license: mit
+base_model: summerMC/TRM-textV2
+tags:
+- text-generation
+- trm-text
+- ism
+---
+# TRM-textV2: Recurrent Shared Transformer with ISM
+This model is a Recurrent Shared Transformer trained with the Inverse Square Mask (ISM) logic. It uses a single Transformer block repeated multiple times (recurrence_steps=4) to simulate depth while maintaining a lower parameter count.
+## Key Features
+- **Architecture**: Shared Recurrent Transformer Block (v34).
+- **Inference**: Supports ISM-based prefix-answer masking.
+- **Training**: TinyStories & FineWeb optimized.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained('summerMC/TRM-textV2', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('summerMC/TRM-textV2')
 ```