Instructions to use Vasanth/chessdevilai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vasanth/chessdevilai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vasanth/chessdevilai")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vasanth/chessdevilai") model = AutoModelForCausalLM.from_pretrained("Vasanth/chessdevilai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vasanth/chessdevilai with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vasanth/chessdevilai" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vasanth/chessdevilai", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vasanth/chessdevilai
- SGLang
How to use Vasanth/chessdevilai 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 "Vasanth/chessdevilai" \ --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": "Vasanth/chessdevilai", "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 "Vasanth/chessdevilai" \ --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": "Vasanth/chessdevilai", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vasanth/chessdevilai with Docker Model Runner:
docker model run hf.co/Vasanth/chessdevilai
File size: 6,409 Bytes
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license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- generated_from_trainer
model-index:
- name: chessdevilai
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chessdevilai
This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7609
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1912 | 0.0100 | 62 | 1.2654 |
| 1.1714 | 0.0200 | 124 | 1.1780 |
| 1.0771 | 0.0301 | 186 | 1.1419 |
| 1.0829 | 0.0401 | 248 | 1.1046 |
| 1.0113 | 0.0501 | 310 | 1.0850 |
| 1.152 | 0.0601 | 372 | 1.0701 |
| 1.0895 | 0.0701 | 434 | 1.0544 |
| 0.9123 | 0.0802 | 496 | 1.0484 |
| 1.0489 | 0.0902 | 558 | 1.0214 |
| 1.0312 | 0.1002 | 620 | 1.0252 |
| 0.9756 | 0.1102 | 682 | 1.0020 |
| 1.0125 | 0.1202 | 744 | 0.9940 |
| 1.0581 | 0.1303 | 806 | 0.9862 |
| 1.0726 | 0.1403 | 868 | 0.9809 |
| 0.9963 | 0.1503 | 930 | 0.9830 |
| 0.9309 | 0.1603 | 992 | 0.9653 |
| 0.8858 | 0.1703 | 1054 | 0.9538 |
| 1.1137 | 0.1803 | 1116 | 0.9472 |
| 0.9024 | 0.1904 | 1178 | 0.9411 |
| 0.9812 | 0.2004 | 1240 | 0.9396 |
| 0.9916 | 0.2104 | 1302 | 0.9254 |
| 0.9509 | 0.2204 | 1364 | 0.9334 |
| 0.8848 | 0.2304 | 1426 | 0.9439 |
| 0.8302 | 0.2405 | 1488 | 0.9175 |
| 1.0111 | 0.2505 | 1550 | 0.9158 |
| 1.0273 | 0.2605 | 1612 | 0.9182 |
| 0.8968 | 0.2705 | 1674 | 0.9116 |
| 0.8892 | 0.2805 | 1736 | 0.9098 |
| 0.7539 | 0.2906 | 1798 | 0.8896 |
| 0.811 | 0.3006 | 1860 | 0.8968 |
| 0.928 | 0.3106 | 1922 | 0.8875 |
| 0.8163 | 0.3206 | 1984 | 0.8821 |
| 0.9202 | 0.3306 | 2046 | 0.8820 |
| 1.0208 | 0.3407 | 2108 | 0.8811 |
| 0.8297 | 0.3507 | 2170 | 0.8823 |
| 0.8213 | 0.3607 | 2232 | 0.8736 |
| 0.8324 | 0.3707 | 2294 | 0.8698 |
| 0.7721 | 0.3807 | 2356 | 0.8735 |
| 0.9504 | 0.3908 | 2418 | 0.8705 |
| 0.858 | 0.4008 | 2480 | 0.8620 |
| 0.8791 | 0.4108 | 2542 | 0.8540 |
| 0.8411 | 0.4208 | 2604 | 0.8606 |
| 0.8845 | 0.4308 | 2666 | 0.8496 |
| 0.7752 | 0.4409 | 2728 | 0.8462 |
| 0.8598 | 0.4509 | 2790 | 0.8481 |
| 0.7935 | 0.4609 | 2852 | 0.8412 |
| 0.7352 | 0.4709 | 2914 | 0.8392 |
| 0.8153 | 0.4809 | 2976 | 0.8426 |
| 0.7371 | 0.4910 | 3038 | 0.8332 |
| 0.7136 | 0.5010 | 3100 | 0.8300 |
| 0.9777 | 0.5110 | 3162 | 0.8294 |
| 0.8336 | 0.5210 | 3224 | 0.8306 |
| 0.7546 | 0.5310 | 3286 | 0.8234 |
| 0.8436 | 0.5410 | 3348 | 0.8237 |
| 0.9316 | 0.5511 | 3410 | 0.8224 |
| 0.6996 | 0.5611 | 3472 | 0.8191 |
| 0.7417 | 0.5711 | 3534 | 0.8146 |
| 0.8528 | 0.5811 | 3596 | 0.8110 |
| 0.6861 | 0.5911 | 3658 | 0.8095 |
| 0.8401 | 0.6012 | 3720 | 0.8096 |
| 0.7056 | 0.6112 | 3782 | 0.8080 |
| 0.8643 | 0.6212 | 3844 | 0.8004 |
| 0.7575 | 0.6312 | 3906 | 0.8018 |
| 0.8133 | 0.6412 | 3968 | 0.8008 |
| 0.8221 | 0.6513 | 4030 | 0.7940 |
| 0.8004 | 0.6613 | 4092 | 0.7948 |
| 0.7002 | 0.6713 | 4154 | 0.7984 |
| 0.8425 | 0.6813 | 4216 | 0.7892 |
| 0.6777 | 0.6913 | 4278 | 0.7876 |
| 0.9178 | 0.7014 | 4340 | 0.7865 |
| 0.787 | 0.7114 | 4402 | 0.7844 |
| 0.6979 | 0.7214 | 4464 | 0.7829 |
| 0.7954 | 0.7314 | 4526 | 0.7825 |
| 0.7937 | 0.7414 | 4588 | 0.7792 |
| 0.7849 | 0.7515 | 4650 | 0.7790 |
| 0.7108 | 0.7615 | 4712 | 0.7782 |
| 0.831 | 0.7715 | 4774 | 0.7768 |
| 0.8242 | 0.7815 | 4836 | 0.7741 |
| 0.7472 | 0.7915 | 4898 | 0.7731 |
| 0.8171 | 0.8016 | 4960 | 0.7732 |
| 0.7857 | 0.8116 | 5022 | 0.7702 |
| 0.7925 | 0.8216 | 5084 | 0.7707 |
| 0.7134 | 0.8316 | 5146 | 0.7680 |
| 0.8401 | 0.8416 | 5208 | 0.7686 |
| 0.6919 | 0.8516 | 5270 | 0.7679 |
| 0.7689 | 0.8617 | 5332 | 0.7658 |
| 0.7899 | 0.8717 | 5394 | 0.7645 |
| 0.8457 | 0.8817 | 5456 | 0.7639 |
| 0.7738 | 0.8917 | 5518 | 0.7635 |
| 0.7943 | 0.9017 | 5580 | 0.7628 |
| 0.756 | 0.9118 | 5642 | 0.7625 |
| 0.8021 | 0.9218 | 5704 | 0.7619 |
| 0.7325 | 0.9318 | 5766 | 0.7615 |
| 0.7312 | 0.9418 | 5828 | 0.7613 |
| 0.8255 | 0.9518 | 5890 | 0.7613 |
| 0.794 | 0.9619 | 5952 | 0.7610 |
| 0.7392 | 0.9719 | 6014 | 0.7609 |
| 0.841 | 0.9819 | 6076 | 0.7609 |
| 0.7018 | 0.9919 | 6138 | 0.7609 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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