Instructions to use ralyn/NPComposer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ralyn/NPComposer-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ralyn/NPComposer-v2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ralyn/NPComposer-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ralyn/NPComposer-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ralyn/NPComposer-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ralyn/NPComposer-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ralyn/NPComposer-v2
- SGLang
How to use ralyn/NPComposer-v2 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 "ralyn/NPComposer-v2" \ --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": "ralyn/NPComposer-v2", "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 "ralyn/NPComposer-v2" \ --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": "ralyn/NPComposer-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ralyn/NPComposer-v2 with Docker Model Runner:
docker model run hf.co/ralyn/NPComposer-v2
Model save
Browse files
README.md
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/npcomposer/npcomposer/runs/lpvwn86m)
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# NPComposer-v2
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This model is a fine-tuned version of [ibm-research/GP-MoLFormer-Uniq](https://huggingface.co/ibm-research/GP-MoLFormer-Uniq) on
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## Model description
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 8.0
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### Framework versions
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- Transformers 4.44.2
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/npcomposer/npcomposer/runs/lpvwn86m)
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# NPComposer-v2
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This model is a fine-tuned version of [ibm-research/GP-MoLFormer-Uniq](https://huggingface.co/ibm-research/GP-MoLFormer-Uniq) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2718
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## Model description
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 8.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:------:|:---------------:|
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| 0.4069 | 1.0 | 27763 | 0.3820 |
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| 0.3507 | 2.0 | 55526 | 0.3371 |
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| 0.3247 | 3.0 | 83289 | 0.3142 |
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| 0.3074 | 4.0 | 111052 | 0.2995 |
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| 0.291 | 5.0 | 138815 | 0.2888 |
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| 0.2765 | 6.0 | 166578 | 0.2803 |
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| 0.2677 | 7.0 | 194341 | 0.2750 |
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| 0.2569 | 8.0 | 222104 | 0.2718 |
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### Framework versions
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- Transformers 4.44.2
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