Instructions to use w-ahmad/A-mlp-silu-9L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w-ahmad/A-mlp-silu-9L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="w-ahmad/A-mlp-silu-9L")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("w-ahmad/A-mlp-silu-9L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use w-ahmad/A-mlp-silu-9L 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-silu-9L" # 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-silu-9L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/w-ahmad/A-mlp-silu-9L
- SGLang
How to use w-ahmad/A-mlp-silu-9L 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-silu-9L" \ --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-silu-9L", "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-silu-9L" \ --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-silu-9L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use w-ahmad/A-mlp-silu-9L with Docker Model Runner:
docker model run hf.co/w-ahmad/A-mlp-silu-9L
A-mlp-silu-9L
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4392
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 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 |
|---|---|---|---|
| 5.9348 | 0.0270 | 50 | 5.4022 |
| 4.5723 | 0.0539 | 100 | 4.4221 |
| 4.1315 | 0.0809 | 150 | 3.9431 |
| 3.6815 | 0.1078 | 200 | 3.6256 |
| 3.4972 | 0.1348 | 250 | 3.4073 |
| 3.2796 | 0.1617 | 300 | 3.2631 |
| 3.1661 | 0.1887 | 350 | 3.1170 |
| 3.0209 | 0.2156 | 400 | 3.0168 |
| 2.9489 | 0.2426 | 450 | 2.9210 |
| 2.8509 | 0.2695 | 500 | 2.8447 |
| 2.7963 | 0.2965 | 550 | 2.7792 |
| 2.7231 | 0.3235 | 600 | 2.7174 |
| 2.6832 | 0.3504 | 650 | 2.6621 |
| 2.6222 | 0.3774 | 700 | 2.6137 |
| 2.5946 | 0.4043 | 750 | 2.5820 |
| 2.5398 | 0.4313 | 800 | 2.5429 |
| 2.5232 | 0.4582 | 850 | 2.5085 |
| 2.4835 | 0.4852 | 900 | 2.4862 |
| 2.4667 | 0.5121 | 950 | 2.4633 |
| 2.4404 | 0.5391 | 1000 | 2.4392 |
Framework versions
- Transformers 5.15.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 5.0.1
- Tokenizers 0.22.2
- Downloads last month
- 84