Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
Training Details
- Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 (1.1B parameters)
- Dataset: tatsu-lab/alpaca — 5,000 samples (4,500 train / 500 val, seed=42)
- Method: Supervised Fine-Tuning (SFT) with LoRA via PEFT + TRL
Usage
python
from transformers import pipelinepipe = pipeline("text-generation", model="Alizahh/tinyllama-alpaca-sft-A1")prompt = "### Instruction:\nExplain what inflation means in simple terms.\n\n### Response:\n"result = pipe(prompt, max_new_tokens=150, do_sample=False)print(result[0]["generated_text"])
Model provider
Alizahh
Model tree
Base
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information