Text Generation
Transformers
Safetensors
llama
trl
sft
Generated from Trainer
text-generation-inference
Instructions to use nandmnnit/testModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nandmnnit/testModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nandmnnit/testModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nandmnnit/testModel") model = AutoModelForCausalLM.from_pretrained("nandmnnit/testModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nandmnnit/testModel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nandmnnit/testModel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nandmnnit/testModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nandmnnit/testModel
- SGLang
How to use nandmnnit/testModel 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 "nandmnnit/testModel" \ --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": "nandmnnit/testModel", "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 "nandmnnit/testModel" \ --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": "nandmnnit/testModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nandmnnit/testModel with Docker Model Runner:
docker model run hf.co/nandmnnit/testModel
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Download README.md from nandmnnit/testModel: direct link, hf CLI and curl.
- Browser
- Download file 993 Bytes
-
https://huggingface.co/nandmnnit/testModel/resolve/main/README.md
- Command line
-
hf download hf://nandmnnit/testModel/README.md
-
curl -L -o README.md https://huggingface.co/nandmnnit/testModel/resolve/main/README.md
993 Bytes
metadata
library_name: transformers
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: testModel
results: []
testModel
This model is a fine-tuned version of on an unknown dataset.
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.1