Instructions to use w-ahmad/A-mlp-sigmoid-46L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w-ahmad/A-mlp-sigmoid-46L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="w-ahmad/A-mlp-sigmoid-46L")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("w-ahmad/A-mlp-sigmoid-46L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use w-ahmad/A-mlp-sigmoid-46L with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "w-ahmad/A-mlp-sigmoid-46L" # 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/A-mlp-sigmoid-46L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/w-ahmad/A-mlp-sigmoid-46L
- SGLang
How to use w-ahmad/A-mlp-sigmoid-46L 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/A-mlp-sigmoid-46L" \ --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/A-mlp-sigmoid-46L", "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/A-mlp-sigmoid-46L" \ --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/A-mlp-sigmoid-46L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use w-ahmad/A-mlp-sigmoid-46L with Docker Model Runner:
docker model run hf.co/w-ahmad/A-mlp-sigmoid-46L
A-mlp-sigmoid-46L
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1332
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.0847 | 0.0270 | 50 | 5.9759 |
| 5.4832 | 0.0539 | 100 | 5.3702 |
| 5.0740 | 0.0809 | 150 | 4.8471 |
| 4.4804 | 0.1078 | 200 | 4.3952 |
| 4.2398 | 0.1348 | 250 | 4.1300 |
| 4.0013 | 0.1617 | 300 | 3.9708 |
| 3.8846 | 0.1887 | 350 | 3.8692 |
| 3.7586 | 0.2156 | 400 | 3.7299 |
| 3.6900 | 0.2426 | 450 | 3.6492 |
| 3.5804 | 0.2695 | 500 | 3.5908 |
| 3.5253 | 0.2965 | 550 | 3.5110 |
| 3.4774 | 0.3235 | 600 | 3.4466 |
| 3.4235 | 0.3504 | 650 | 3.3978 |
| 3.3666 | 0.3774 | 700 | 3.3445 |
| 3.3126 | 0.4043 | 750 | 3.2957 |
| 3.2650 | 0.4313 | 800 | 3.2709 |
| 3.2571 | 0.4582 | 850 | 3.2328 |
| 3.2067 | 0.4852 | 900 | 3.2244 |
| 3.1642 | 0.5121 | 950 | 3.1718 |
| 3.1494 | 0.5391 | 1000 | 3.1332 |
Framework versions
- Transformers 5.15.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 5.0.1
- Tokenizers 0.22.2
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