Text Generation
Transformers
PyTorch
Safetensors
English
qwen2
qwen2.5
7B
Instruct
Math
CoT
one-shot
conversational
text-generation-inference
Instructions to use prithivMLmods/Math-IIO-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Math-IIO-7B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/Math-IIO-7B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Math-IIO-7B-Instruct") model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Math-IIO-7B-Instruct") 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
- vLLM
How to use prithivMLmods/Math-IIO-7B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Math-IIO-7B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Math-IIO-7B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/Math-IIO-7B-Instruct
- SGLang
How to use prithivMLmods/Math-IIO-7B-Instruct 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 "prithivMLmods/Math-IIO-7B-Instruct" \ --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": "prithivMLmods/Math-IIO-7B-Instruct", "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 "prithivMLmods/Math-IIO-7B-Instruct" \ --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": "prithivMLmods/Math-IIO-7B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use prithivMLmods/Math-IIO-7B-Instruct with Docker Model Runner:
docker model run hf.co/prithivMLmods/Math-IIO-7B-Instruct
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#### **Math IIO 7B Instruct Model Files Upload Details:**
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| File Name | Size | Description | Upload Status |
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| `.gitattributes` | 1.57 kB | Git attributes configuration file | Uploaded |
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| `README.md` | 263 Bytes | README file with minimal details | Updated |
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| `added_tokens.json` | 657 Bytes | Custom added tokens for tokenizer | Uploaded |
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| `config.json` | 861 Bytes | Model configuration file | Uploaded |
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| `generation_config.json` | 281 Bytes | Configuration for text generation settings | Uploaded |
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| `merges.txt` | 1.82 MB | Merge rules for byte pair encoding tokenizer | Uploaded |
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| `pytorch_model-00001-of-00004.bin` | 4.88 GB | First part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00002-of-00004.bin` | 4.93 GB | Second part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00003-of-00004.bin` | 4.33 GB | Third part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00004-of-00004.bin` | 1.09 GB | Fourth part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 28.1 kB | Index JSON file for model weights | Uploaded |
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| `special_tokens_map.json` | 644 Bytes | Map of special tokens used by the tokenizer | Uploaded |
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| `tokenizer.json` | 11.4 MB | Tokenizer settings and vocab | Uploaded (LFS) |
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| `tokenizer_config.json` | 7.73 kB | Configuration for tokenizer | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary for tokenizer | Uploaded |
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