Text Generation
Transformers
PyTorch
Russian
English
t5
text2text-generation
text-generation-inference
Instructions to use AlexWortega/FlanFred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/FlanFred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AlexWortega/FlanFred")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlexWortega/FlanFred") model = AutoModelForSeq2SeqLM.from_pretrained("AlexWortega/FlanFred", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AlexWortega/FlanFred with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AlexWortega/FlanFred" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlexWortega/FlanFred", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AlexWortega/FlanFred
- SGLang
How to use AlexWortega/FlanFred 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 "AlexWortega/FlanFred" \ --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": "AlexWortega/FlanFred", "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 "AlexWortega/FlanFred" \ --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": "AlexWortega/FlanFred", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AlexWortega/FlanFred with Docker Model Runner:
docker model run hf.co/AlexWortega/FlanFred
Commit ·
cdf01e9
1
Parent(s): cde8f6c
Update README.md
Browse files
README.md
CHANGED
|
@@ -51,12 +51,12 @@ print(generate_text(input_str, t5_tokenizer, t5_model, device))
|
|
| 51 |
| xwinograd_ru | 0.66 |0.51 |0.54 |
|
| 52 |
```
|
| 53 |
|
| 54 |
-
#
|
| 55 |
```
|
| 56 |
-
@MISC{
|
| 57 |
-
author = {
|
| 58 |
-
title = {Russian
|
| 59 |
-
url = {https://huggingface.co/
|
| 60 |
year = 2023
|
| 61 |
}
|
| 62 |
```
|
|
|
|
| 51 |
| xwinograd_ru | 0.66 |0.51 |0.54 |
|
| 52 |
```
|
| 53 |
|
| 54 |
+
# Citation
|
| 55 |
```
|
| 56 |
+
@MISC{AlexWortega/flan_translated_300k,
|
| 57 |
+
author = {Pavel Ilin, Ksenia Zolian,Ilya kuleshov, Egor Kokush, Aleksandr Nikolich},
|
| 58 |
+
title = {Russian Flan translated},
|
| 59 |
+
url = {https://huggingface.co/datasets/AlexWortega/flan_translated_300k},
|
| 60 |
year = 2023
|
| 61 |
}
|
| 62 |
```
|