Text Generation
Transformers
Safetensors
opt
trl
sft
Generated from Trainer
text-generation-inference
Instructions to use Kinjal123/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kinjal123/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kinjal123/content")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kinjal123/content") model = AutoModelForCausalLM.from_pretrained("Kinjal123/content") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kinjal123/content with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kinjal123/content" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kinjal123/content", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kinjal123/content
- SGLang
How to use Kinjal123/content 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 "Kinjal123/content" \ --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": "Kinjal123/content", "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 "Kinjal123/content" \ --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": "Kinjal123/content", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kinjal123/content with Docker Model Runner:
docker model run hf.co/Kinjal123/content
content
This model is a fine-tuned version of facebook/opt-350m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5972
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.0419 | 0.2 | 50 | 3.8081 |
| 3.816 | 0.4 | 100 | 3.7579 |
| 3.78 | 0.6 | 150 | 3.7016 |
| 3.753 | 0.8 | 200 | 3.6749 |
| 3.6787 | 1.0 | 250 | 3.6132 |
| 2.987 | 1.2 | 300 | 3.6374 |
| 3.0092 | 1.4 | 350 | 3.6043 |
| 3.0088 | 1.6 | 400 | 3.5676 |
| 2.945 | 1.8 | 450 | 3.5404 |
| 2.9204 | 2.0 | 500 | 3.5082 |
| 2.2216 | 2.2 | 550 | 3.6194 |
| 2.212 | 2.4 | 600 | 3.6117 |
| 2.198 | 2.6 | 650 | 3.6019 |
| 2.1787 | 2.8 | 700 | 3.5973 |
| 2.1878 | 3.0 | 750 | 3.5972 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Kinjal123/content
Base model
facebook/opt-350m