Instructions to use w-ahmad/2M-ACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w-ahmad/2M-ACT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="w-ahmad/2M-ACT")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("w-ahmad/2M-ACT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use w-ahmad/2M-ACT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "w-ahmad/2M-ACT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "w-ahmad/2M-ACT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/w-ahmad/2M-ACT
- SGLang
How to use w-ahmad/2M-ACT 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 "w-ahmad/2M-ACT" \ --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": "w-ahmad/2M-ACT", "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 "w-ahmad/2M-ACT" \ --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": "w-ahmad/2M-ACT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use w-ahmad/2M-ACT with Docker Model Runner:
docker model run hf.co/w-ahmad/2M-ACT
Model save
Browse files- README.md +11 -22
- training_log.jsonl +2 -2
README.md
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant
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- training_steps:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 3.3162 | 0.0135 | 200 | 3.1383 |
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| 0.2141 | 0.1618 | 2400 | 0.2089 |
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| 0.1972 | 0.1753 | 2600 | 0.1989 |
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| 0.1940 | 0.1887 | 2800 | 0.1959 |
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| 0.1902 | 0.2022 | 3000 | 0.1934 |
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| 0.1860 | 0.2562 | 3800 | 0.1876 |
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| 0.1863 | 0.2696 | 4000 | 0.1866 |
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| 0.1849 | 0.2761 | 4096 | 0.1862 |
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### Framework versions
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2160
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: constant
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- training_steps: 2000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 3.3162 | 0.0135 | 200 | 3.1383 |
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| 0.8389 | 0.0270 | 400 | 0.7973 |
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| 0.4117 | 0.0404 | 600 | 0.4067 |
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| 0.3285 | 0.0539 | 800 | 0.3293 |
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| 0.2877 | 0.0674 | 1000 | 0.2870 |
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| 0.2144 | 0.1348 | 2000 | 0.2160 |
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### Framework versions
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training_log.jsonl
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