metadata
library_name: pruna-engine
thumbnail: >-
https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
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Frequently Asked Questions
- How does the compression work? The model is compressed by using bitsandbytes.
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- What is the model format? We the standard safetensors format.
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Usage
Here's how you can run the model use the model:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("PrunaAI/stable-code-instruct-3b-bnb-4bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("PrunaAI/stable-code-instruct-3b-bnb-4bit", torch_dtype=torch.bfloat16, trust_remote_code=True)
model.eval()
model = model.cuda()
messages = [
{
"role": "system",
"content": "You are a helpful and polite assistant",
},
{
"role": "user",
"content": "Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes."
},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
tokens = model.generate(
**inputs,
max_new_tokens=1024,
temperature=0.5,
top_p=0.95,
top_k=100,
do_sample=True,
use_cache=True
)
output = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0]
Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model stabilityai/stable-code-instruct-3b before using this model which provided the base model. The license of the pruna-engine is here on Pypi.