Text Generation
Transformers
Safetensors
PyTorch
English
qwen3
reasoning
math
coding
instruction-tuned
conversational
text-generation-inference
Instructions to use Surpem/Supertron1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Surpem/Supertron1-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Surpem/Supertron1-8B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Surpem/Supertron1-8B") model = AutoModelForCausalLM.from_pretrained("Surpem/Supertron1-8B", 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Surpem/Supertron1-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Surpem/Supertron1-8B" # 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/Supertron1-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Surpem/Supertron1-8B
- SGLang
How to use Surpem/Supertron1-8B 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/Supertron1-8B" \ --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/Supertron1-8B", "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/Supertron1-8B" \ --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/Supertron1-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Surpem/Supertron1-8B with Docker Model Runner:
docker model run hf.co/Surpem/Supertron1-8B
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages[0].role == 'system' %} | |
| {{- messages[0].content + '\n\n' }} | |
| {%- endif %} | |
| {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>" }} | |
| {%- for tool in tools %} | |
| {{- "\n" }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }} | |
| {%- else %} | |
| {%- if messages[0].role == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set ns = namespace(multi_step_tool_call=false, last_query_index=messages|length - 1) %} | |
| {%- for message in messages[::-1] %} | |
| {%- if message.role == 'user' and not ns.multi_step_tool_call %} | |
| {%- set ns.last_query_index = loop.revindex0 %} | |
| {%- break %} | |
| {%- elif message.role == 'assistant' and message.tool_calls %} | |
| {%- set ns.multi_step_tool_call = true %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- for message in messages %} | |
| {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %} | |
| {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == 'assistant' %} | |
| {%- set content = message.content %} | |
| {%- set reasoning_content = '' %} | |
| {%- if message.reasoning_content is defined and message.reasoning_content %} | |
| {%- set reasoning_content = message.reasoning_content %} | |
| {%- elif content and content | length > 0 and loop.index0 < ns.last_query_index %} | |
| {%- if '</think>' in content %} | |
| {%- set reasoning_content = (content | regex_search('<think>(.*?)</think>', multiline=true, dotall=true) or [''])[0] %} | |
| {%- set content = content | regex_replace('<think>.*?</think>', '', multiline=true, dotall=true) | trim %} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- if loop.index0 < ns.last_query_index %} | |
| {%- if reasoning_content %} | |
| {{- '<|im_start|>assistant\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') + '<|im_end|>\n' }} | |
| {%- else %} | |
| {{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' + content.lstrip('\n') + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- else %} | |
| {%- if reasoning_content %} | |
| {{- '<|im_start|>assistant\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') + '<|im_end|>\n' }} | |
| {%- elif message.tool_calls %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if tool_call.function is defined %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '<tool_call>\n{"name": "' + tool_call.name + '", "arguments": ' }} | |
| {%- if tool_call.arguments is string %} | |
| {{- tool_call.arguments }} | |
| {%- else %} | |
| {{- tool_call.arguments | tojson }} | |
| {%- endif %} | |
| {{- '}\n</tool_call>' }} | |
| {%- if not loop.last %} | |
| {{- '\n' }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {{- '<|im_end|>\n' }} | |
| {%- else %} | |
| {{- '<|im_start|>assistant\n' + content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- elif message.role == 'tool' %} | |
| {%- if loop.first or messages[loop.index0 - 1].role != 'tool' %} | |
| {{- '<|im_start|>user\n' }} | |
| {%- endif %} | |
| {{- '<tool_response>\n' + message.content + '\n</tool_response>' }} | |
| {%- if loop.last or messages[loop.index0 + 1].role != 'tool' %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- if enable_thinking is defined and enable_thinking is false %} | |
| {{- '<think>\n\n</think>\n\n' }} | |
| {%- endif %} | |
| {%- endif %} | |