Instructions to use LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5 with Transformers:

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

pipe = pipeline("text-generation", model="LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5

SGLang

How to use LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5 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 "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5" \
    --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": "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5",
		"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 "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5" \
        --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": "LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5 with Docker Model Runner:
```
docker model run hf.co/LLM-course/TRM_d48_L1_H2_C4_35k_LegalW0p5
```

Chess model submitted to the LLM Course Chess Challenge.

Submission Info

Submitted by: janisaiad
Parameters: 43,104
Organization: LLM-course

Model Details

Architecture: Tiny Recursive Model (TRM) - looping recurrent transformer (cycle-shared weights)
Vocab size: 148
Embedding dim: 48
Layers: 1
Heads: 2
Cycles: 4

TRM note: this is a looping TRM model — at inference/training time we run the same transformer stack for 4 recurrent refinement cycle(s) (weights are shared across cycles), which increases compute/reasoning depth without increasing parameter count.

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