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---
base_model: unsloth/gemma-2-27b-it-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
- sft
---
# Model Specifications
- **Max Sequence Length**: Trained at 16384 (via RoPE Scaling)
- **Data Type**: Auto detection, with options for Float16 and Bfloat16
- **Quantization**: 4bit, to reduce memory usage
## Training Data
Used a private dataset with hundreds of technical tutorials and associated summaries.
## Implementation Highlights
- **Efficiency**: Emphasis on reducing memory usage and accelerating download speeds through 4bit quantization.
- **Adaptability**: Auto detection of data types and support for advanced configuration options like RoPE scaling, LoRA, and gradient checkpointing.
# Uploaded Model
- **Developed by:** ndebuhr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-27b-it-bnb-4bit
# Configuration and Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
input_text = ""
# Set device based on CUDA availability
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the model and tokenizer
model_name = "ndebuhr/Gemma-2-27B-Technical-Tutorial-Summarization-QLoRA"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
instruction = "Clarify and summarize this tutorial transcript"
prompt = """{}
### Raw Transcript:
{}
### Summary:
"""
# Tokenize the input text
inputs = tokenizer(
prompt.format(instruction, input_text),
return_tensors="pt",
truncation=True,
max_length=16384
).to(device)
# Generate outputs
outputs = model.generate(
**inputs,
max_length=16384,
num_return_sequences=1,
use_cache=True
)
# Decode the generated text
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
```
## Compute Infrastructure
* Fine-tuning: used 1xA100 (40GB)
* Inference: recommend 1xL4 (24GB)
This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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