Text Generation
Transformers
PyTorch
gpt2
CodeGPT-small-py
hearthstone
Eval Results (legacy)
text-generation-inference
Instructions to use dvitel/h0-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvitel/h0-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dvitel/h0-1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dvitel/h0-1") model = AutoModelForCausalLM.from_pretrained("dvitel/h0-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dvitel/h0-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dvitel/h0-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dvitel/h0-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dvitel/h0-1
- SGLang
How to use dvitel/h0-1 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 "dvitel/h0-1" \ --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": "dvitel/h0-1", "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 "dvitel/h0-1" \ --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": "dvitel/h0-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dvitel/h0-1 with Docker Model Runner:
docker model run hf.co/dvitel/h0-1
update model card README.md
Browse files
README.md
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---
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tags:
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- generated_from_trainer
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metrics:
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- bleu
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model-index:
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- name: h0-1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# h0-1
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This model is a fine-tuned version of [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3622
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- Exact Match: 0.1970
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- Bleu: 0.9193
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- Codebleu: 0.7686
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- Chrf: 93.5686
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 17
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- num_epochs: 200
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Chrf |
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|:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-------:|
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| 0.2482 | 11.94 | 1600 | 0.2828 | 0.1364 | 0.9012 | 0.7012 | 92.2247 |
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| 0.0203 | 23.88 | 3200 | 0.2968 | 0.1970 | 0.9114 | 0.7298 | 93.0236 |
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| 0.0082 | 35.82 | 4800 | 0.3049 | 0.1970 | 0.9125 | 0.7480 | 93.1997 |
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| 0.0049 | 47.76 | 6400 | 0.3190 | 0.1818 | 0.9125 | 0.7526 | 93.0967 |
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| 0.0038 | 59.7 | 8000 | 0.3289 | 0.1818 | 0.9117 | 0.7348 | 93.1293 |
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| 0.0024 | 71.64 | 9600 | 0.3358 | 0.1970 | 0.9142 | 0.7555 | 93.0747 |
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| 0.0022 | 83.58 | 11200 | 0.3379 | 0.1970 | 0.9164 | 0.7642 | 93.2931 |
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| 0.0013 | 95.52 | 12800 | 0.3444 | 0.2121 | 0.9189 | 0.7700 | 93.4456 |
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| 0.0009 | 107.46 | 14400 | 0.3408 | 0.1970 | 0.9188 | 0.7655 | 93.4808 |
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| 0.0006 | 119.4 | 16000 | 0.3522 | 0.1970 | 0.9177 | 0.7510 | 93.4061 |
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| 0.0003 | 131.34 | 17600 | 0.3589 | 0.2121 | 0.9178 | 0.7614 | 93.3980 |
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| 0.0002 | 143.28 | 19200 | 0.3562 | 0.2121 | 0.9179 | 0.7634 | 93.5130 |
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| 0.0002 | 155.22 | 20800 | 0.3624 | 0.1970 | 0.9208 | 0.7699 | 93.6707 |
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| 0.0001 | 167.16 | 22400 | 0.3608 | 0.1970 | 0.9193 | 0.7703 | 93.6082 |
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| 0.0001 | 179.1 | 24000 | 0.3620 | 0.1970 | 0.9190 | 0.7667 | 93.5154 |
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| 0.0001 | 191.04 | 25600 | 0.3622 | 0.1970 | 0.9193 | 0.7686 | 93.5686 |
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### Framework versions
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- Transformers 4.24.0
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- Pytorch 1.13.0
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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