Instructions to use Ashishkr/grammar_correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ashishkr/grammar_correction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ashishkr/grammar_correction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ashishkr/grammar_correction") model = AutoModelForCausalLM.from_pretrained("Ashishkr/grammar_correction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ashishkr/grammar_correction with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ashishkr/grammar_correction" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ashishkr/grammar_correction", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ashishkr/grammar_correction
- SGLang
How to use Ashishkr/grammar_correction 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 "Ashishkr/grammar_correction" \ --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": "Ashishkr/grammar_correction", "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 "Ashishkr/grammar_correction" \ --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": "Ashishkr/grammar_correction", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ashishkr/grammar_correction with Docker Model Runner:
docker model run hf.co/Ashishkr/grammar_correction
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
|
| 3 |
+
license: apache-2.0
|
| 4 |
+
inference: false
|
| 5 |
+
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
```
|
| 10 |
+
from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModelForCausalLM
|
| 11 |
+
import torch
|
| 12 |
+
if torch.cuda.is_available():
|
| 13 |
+
device = torch.device("cuda")
|
| 14 |
+
else :
|
| 15 |
+
device = "cpu"
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
tokenizer = AutoTokenizer.from_pretrained("salesken/grammar_correction")
|
| 19 |
+
model = AutoModelForCausalLM.from_pretrained("salesken/grammar_correction").to(device)
|
| 20 |
+
|
| 21 |
+
input_query="what be the reason for everyone leave the company"
|
| 22 |
+
query= "<|startoftext|> " + input_query + " ~~~"
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
input_ids = tokenizer.encode(query.lower(), return_tensors='pt').to(device)
|
| 26 |
+
sample_outputs = model.generate(input_ids,
|
| 27 |
+
do_sample=True,
|
| 28 |
+
num_beams=1,
|
| 29 |
+
max_length=128,
|
| 30 |
+
temperature=0.9,
|
| 31 |
+
top_p= 0.7,
|
| 32 |
+
top_k = 5,
|
| 33 |
+
num_return_sequences=3)
|
| 34 |
+
corrected_sentences = []
|
| 35 |
+
for i in range(len(sample_outputs)):
|
| 36 |
+
r = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split('||')[0]
|
| 37 |
+
r = r.split('~~~')[1]
|
| 38 |
+
if r not in corrected_sentences:
|
| 39 |
+
corrected_sentences.append(r)
|
| 40 |
+
|
| 41 |
+
print(corrected_sentences)
|
| 42 |
+
```
|