Instructions to use swadeshb/tivd-gsm8k-colocate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use swadeshb/tivd-gsm8k-colocate with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "swadeshb/tivd-gsm8k-colocate") - Transformers
How to use swadeshb/tivd-gsm8k-colocate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swadeshb/tivd-gsm8k-colocate") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("swadeshb/tivd-gsm8k-colocate", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use swadeshb/tivd-gsm8k-colocate with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "swadeshb/tivd-gsm8k-colocate" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swadeshb/tivd-gsm8k-colocate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/swadeshb/tivd-gsm8k-colocate
- SGLang
How to use swadeshb/tivd-gsm8k-colocate 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 "swadeshb/tivd-gsm8k-colocate" \ --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": "swadeshb/tivd-gsm8k-colocate", "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 "swadeshb/tivd-gsm8k-colocate" \ --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": "swadeshb/tivd-gsm8k-colocate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use swadeshb/tivd-gsm8k-colocate with Docker Model Runner:
docker model run hf.co/swadeshb/tivd-gsm8k-colocate
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| "difficulty_threshold": 6.0, | |
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| "fp16_opt_level": "O1", | |
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| "logging_dir": "outputs/tivd_gsm8k_colocate/runs/Apr15_08-45-31_e2e-72-102", | |
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| "lr_scheduler_type": "linear", | |
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| "max_prompt_length": 768, | |
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| "student_model_name_or_path": "Qwen/Qwen3-1.7B", | |
| "system_prompt": "You are a careful mathematical reasoner. Solve the problem step by step. End with a concise final answer in the form Final Answer: \\boxed{...}.", | |
| "target_ema_decay": 0.99, | |
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