transformers safetensors mistral text-generation text-generation-inference unsloth trl sft conversational en base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---

# Model Specifications

- **Max Sequence Length**: 16384 (with auto support for 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/mistral-7b-instruct-v0.2-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/Mistral-7B-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 mistral 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)
