Instructions to use w-ahmad/A-glu-situglu-9L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w-ahmad/A-glu-situglu-9L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="w-ahmad/A-glu-situglu-9L")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("w-ahmad/A-glu-situglu-9L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use w-ahmad/A-glu-situglu-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-glu-situglu-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-glu-situglu-9L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/w-ahmad/A-glu-situglu-9L
- SGLang
How to use w-ahmad/A-glu-situglu-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-glu-situglu-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-glu-situglu-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-glu-situglu-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-glu-situglu-9L", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use w-ahmad/A-glu-situglu-9L with Docker Model Runner:
docker model run hf.co/w-ahmad/A-glu-situglu-9L
A-glu-situglu-9L
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2438
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 |
|---|---|---|---|
| 5.9977 | 0.0270 | 50 | 5.5254 |
| 4.5559 | 0.0539 | 100 | 4.3793 |
| 4.0178 | 0.0809 | 150 | 3.8191 |
| 3.5219 | 0.1078 | 200 | 3.4547 |
| 3.3062 | 0.1348 | 250 | 3.2277 |
| 3.0567 | 0.1617 | 300 | 3.0156 |
| 2.9281 | 0.1887 | 350 | 2.8656 |
| 2.7709 | 0.2156 | 400 | 2.7569 |
| 2.7015 | 0.2426 | 450 | 2.6739 |
| 2.6046 | 0.2695 | 500 | 2.5938 |
| 2.5586 | 0.2965 | 550 | 2.5315 |
| 2.4855 | 0.3235 | 600 | 2.4803 |
| 2.4519 | 0.3504 | 650 | 2.4318 |
| 2.4034 | 0.3774 | 700 | 2.3945 |
| 2.3789 | 0.4043 | 750 | 2.3648 |
| 2.3293 | 0.4313 | 800 | 2.3325 |
| 2.3141 | 0.4582 | 850 | 2.3058 |
| 2.2787 | 0.4852 | 900 | 2.2838 |
| 2.2656 | 0.5121 | 950 | 2.2681 |
| 2.2440 | 0.5391 | 1000 | 2.2438 |
Framework versions
- Transformers 5.15.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 5.0.1
- Tokenizers 0.22.2
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