Text Generation
Transformers
Safetensors
English
gpt2
Generated from Trainer
text-generation-inference
Instructions to use keethu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keethu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="keethu/results")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("keethu/results") model = AutoModelForCausalLM.from_pretrained("keethu/results") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use keethu/results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "keethu/results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "keethu/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/keethu/results
- SGLang
How to use keethu/results 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 "keethu/results" \ --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": "keethu/results", "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 "keethu/results" \ --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": "keethu/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use keethu/results with Docker Model Runner:
docker model run hf.co/keethu/results
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model-index:
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- name: results
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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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#
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training results
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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model-index:
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- name: results
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results: []
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language:
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- en
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metrics:
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- accuracy
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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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# Results
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the Kubernetes dataset, which is updated in the same hub!
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## Model description
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This model can be used to generate texts related to Kubernetes.
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This would be the first model towards interests in IBN.
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## Intended uses & limitations
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It can be used for the text generation.
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## Training and evaluation data
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This model contains only the training data and no evaluation data.
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## Training procedure
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### Training results
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Training Loss:
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TrainOutput(global_step=3, training_loss=3.4602108001708984, metrics={'train_runtime': 83.5107, 'train_samples_per_second': 0.036, 'train_steps_per_second': 0.036, 'total_flos': 1567752192000.0, 'train_loss': 3.4602108001708984, 'epoch': 3.0})
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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