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shanaka95
Available on FriendliAI
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Model Details
Model Provider
shanaka95
Model Tree
unsloth/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
This model is explicitly trained to follow natural language instructions for text revision tasks. You can prompt it to perform various editing operations:
"Fix grammatical errors in this sentence: ...""Make this text easier to understand: ...""Rewrite this sentence in a different way: ...""Make this text more formal: ...""Improve the flow and coherence of this paragraph: ...""Make this sentence more neutral: ..."While highly capable at text revision, this model is not designed for open-ended chatbot conversations, long-form creative story generation, or answering factual trivia. Its strength lies in structural and stylistic transformations of existing text.
You can easily load and use this model via the transformers library.
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "shanaka95/your-model-name-here" # Replace with your actual repo name # Load tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.float16) # Format the input prompt (Instruction + Source Text)instruction = "Fix all grammatical errors in this text:"source_text = "When I grow up, I start to understand what he said is quite right."prompt = f"{instruction} {source_text}" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Generate the edited textoutputs = model.generate(**inputs, max_new_tokens=128)edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(edited_text)# Expected output: "When I grow up, I will start to understand that what he said is quite right."from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "shanaka95/your-model-name-here" # Replace with your actual repo name # Load tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.float16) # Format the input prompt (Instruction + Source Text)instruction = "Fix all grammatical errors in this text:"source_text = "When I grow up, I start to understand what he said is quite right."prompt = f"{instruction} {source_text}" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Generate the edited textoutputs = model.generate(**inputs, max_new_tokens=128)edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(edited_text)# Expected output: "When I grow up, I will start to understand that what he said is quite right."