Instructions to use Mathoctopus/Cross_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathoctopus/Cross_7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mathoctopus/Cross_7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Mathoctopus/Cross_7B") model = AutoModelForCausalLM.from_pretrained("Mathoctopus/Cross_7B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mathoctopus/Cross_7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mathoctopus/Cross_7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mathoctopus/Cross_7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mathoctopus/Cross_7B
- SGLang
How to use Mathoctopus/Cross_7B 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 "Mathoctopus/Cross_7B" \ --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": "Mathoctopus/Cross_7B", "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 "Mathoctopus/Cross_7B" \ --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": "Mathoctopus/Cross_7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mathoctopus/Cross_7B with Docker Model Runner:
docker model run hf.co/Mathoctopus/Cross_7B
Commit Β·
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Parent(s): e2dafad
Update README.md
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README.md
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@@ -49,9 +49,9 @@ Our dataset and models are all available at Huggingface.
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| 7B-LLaMA 2 | π [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | π [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
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|| π[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|π[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
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| 13B-LLaMA 2 | π [MathOctopus-Parallel-13B] | π [MathOctopus-Cross-13B] |
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|| π[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B
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| 33B-LLaMA 1 | π [MathOctopus-Parallel-33B] | π [MathOctopus-Cross-33B] |
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| 70B-LLaMA 2 | Coming soon! | Coming Soon! |
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*-Parallel refers to our model trained with the parallel-training strategy.
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| MathOctopus<sup>P</sup>-33B | 56.0 | 52.5 |
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| MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
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## Intended Uses
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These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
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|----|---------------------------------------------------------------|---------------------------------------------------------------------------|
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| 50 |
| 7B-LLaMA 2 | π [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | π [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
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| 51 |
|| π[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|π[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
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| 52 |
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| 13B-LLaMA 2 | π [MathOctopus-Parallel-13B](https://huggingface.co/Mathoctopus/Parallel_13B) | π [MathOctopus-Cross-13B](https://huggingface.co/Mathoctopus/Cross_13B) |
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| 53 |
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|| π[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B)|π[MathOctopus-Cross-xRFT-13B]|
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| 54 |
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| 33B-LLaMA 1 | π [MathOctopus-Parallel-33B](https://huggingface.co/Mathoctopus/Parallel_33B) | π [MathOctopus-Cross-33B] |
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| 55 |
| 70B-LLaMA 2 | Coming soon! | Coming Soon! |
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| 56 |
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*-Parallel refers to our model trained with the parallel-training strategy.
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| 120 |
| MathOctopus<sup>P</sup>-33B | 56.0 | 52.5 |
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| 121 |
| MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
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| 122 |
## Intended Uses
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| 123 |
+
These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
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