Text Generation
Transformers
Safetensors
English
lfm2_moe
reasoning
math
coding
instruction-tuned
chat
generalist
surpem
conversational
Instructions to use Surpem/Supertron2.1-8B-A1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Surpem/Supertron2.1-8B-A1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Surpem/Supertron2.1-8B-A1B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Surpem/Supertron2.1-8B-A1B") model = AutoModelForCausalLM.from_pretrained("Surpem/Supertron2.1-8B-A1B", 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 Surpem/Supertron2.1-8B-A1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Surpem/Supertron2.1-8B-A1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Surpem/Supertron2.1-8B-A1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Surpem/Supertron2.1-8B-A1B
- SGLang
How to use Surpem/Supertron2.1-8B-A1B 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 "Surpem/Supertron2.1-8B-A1B" \ --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": "Surpem/Supertron2.1-8B-A1B", "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 "Surpem/Supertron2.1-8B-A1B" \ --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": "Surpem/Supertron2.1-8B-A1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Surpem/Supertron2.1-8B-A1B with Docker Model Runner:
docker model run hf.co/Surpem/Supertron2.1-8B-A1B
| { | |
| "model_name": "Supertron-2.1-8B-A1B", | |
| "repo_id": "inspectiong668/Supertron-2.1-8B-A1B", | |
| "base_model": "LiquidAI/LFM2.5-8B-A1B", | |
| "training_mode": "full_parameter_finetune", | |
| "optimizer": "Adafactor", | |
| "gpu": "H100", | |
| "parameter_count": 8467856128, | |
| "sequence_length": 1024, | |
| "micro_batch_size": 1, | |
| "gradient_accumulation_steps": 8, | |
| "steps": 1240, | |
| "tokens": 10158080, | |
| "tokens_per_second": 5640.341404589016, | |
| "final_loss": 1.1090761423110962, | |
| "elapsed_seconds": 1800.968996617, | |
| "datasets": [ | |
| "HuggingFaceH4/ultrachat_200k", | |
| "Open-Orca/SlimOrca", | |
| "teknium/OpenHermes-2.5", | |
| "TIGER-Lab/MathInstruct", | |
| "iamtarun/python_code_instructions_18k_alpaca", | |
| "Salesforce/xlam-function-calling-60k" | |
| ] | |
| } |