Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use thorirhrafn/gpt_icesum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thorirhrafn/gpt_icesum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thorirhrafn/gpt_icesum")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thorirhrafn/gpt_icesum") model = AutoModelForCausalLM.from_pretrained("thorirhrafn/gpt_icesum") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thorirhrafn/gpt_icesum with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thorirhrafn/gpt_icesum" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thorirhrafn/gpt_icesum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thorirhrafn/gpt_icesum
- SGLang
How to use thorirhrafn/gpt_icesum 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 "thorirhrafn/gpt_icesum" \ --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": "thorirhrafn/gpt_icesum", "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 "thorirhrafn/gpt_icesum" \ --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": "thorirhrafn/gpt_icesum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thorirhrafn/gpt_icesum with Docker Model Runner:
docker model run hf.co/thorirhrafn/gpt_icesum
gpt_icesum
This model is a fine-tuned version of AI-Sweden-Models/gpt-sw3-1.3b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7960
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9148 | 0.22 | 50 | 1.8159 |
| 1.9237 | 0.44 | 100 | 1.8051 |
| 1.8317 | 0.67 | 150 | 1.8006 |
| 2.0264 | 0.89 | 200 | 1.7985 |
| 1.81 | 1.11 | 250 | 1.7961 |
| 1.9393 | 1.33 | 300 | 1.7951 |
| 1.8159 | 1.56 | 350 | 1.7934 |
| 1.8204 | 1.78 | 400 | 1.7959 |
| 1.9231 | 2.0 | 450 | 1.7960 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for thorirhrafn/gpt_icesum
Base model
AI-Sweden-Models/gpt-sw3-1.3b