README.md

base_model: unsloth/mistral-7b-v0.3-bnb-4bit
library_name: peft
pipeline_tag: text-generation
language:
- en
---

# Model Card for Quantized Mistral Fine-Tuned Model

## Model Details

### Model Description

This model is a fine-tuned version of the quantized base model `unsloth/mistral-7b-v0.3-bnb-4bit` using PEFT (Parameter-Efficient Fine-Tuning). The fine-tuning process targeted task-specific optimization while maintaining efficiency and compatibility with resource-constrained environments. This model is well-suited for text generation tasks such as summarization, content generation, or instruction-following.

- **Developed by:** Siddhi Kommuri
- **Shared by:** Siddhi Kommuri
- **Model type:** Quantized language model fine-tuned with PEFT
- **Language(s) (NLP):** English (en)
- **License:** Apache 2.0 (assumed based on Mistral licensing)
- **Fine-tuned from model:** `unsloth/mistral-7b-v0.3-bnb-4bit`

### Model Sources

- **Repository:** [Quantized Mistral Fine-Tuned](https://huggingface.co/coeusk/quantized-mistral-finetuned)
- **Base Model Repository:** [Mistral 7B v0.3 Quantized](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit)
- **Frameworks:** PyTorch, PEFT, Transformers

---

## Uses

### Direct Use

This model is intended for text generation tasks, including:
- Generating concise and relevant highlights from product descriptions.
- Summarizing content into well-structured outputs.
- Following instruction-based prompts for creative or structured content generation.

### Downstream Use

The model can be adapted to specialized domains for:
- Summarization in specific contexts (e.g., e-commerce, reviews).
- Instruction-following generation for business-specific tasks.

### Out-of-Scope Use

- Tasks requiring factual accuracy on real-world knowledge post-2024.
- Scenarios involving sensitive, offensive, or harmful content generation.

---

## Bias, Risks, and Limitations

### Bias

The model may exhibit biases present in the training data, especially in domain-specific terminology or representation.

### Risks

- Possible generation of incorrect or misleading information.
- Limitations in handling multilingual inputs or outputs beyond English.

### Limitations

- Designed for English tasks; performance in other languages is not guaranteed.
- May underperform on tasks requiring detailed factual retrieval.

---

## Recommendations

Users should:
- Validate model outputs for correctness in high-stakes use cases.
- Avoid using the model for critical decision-making without human supervision.

---

## How to Get Started with the Model

### Code Example

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model
base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.3-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "coeusk/quantized-mistral-finetuned")
tokenizer = AutoTokenizer.from_pretrained("unsloth/mistral-7b-v0.3-bnb-4bit")

# Prepare input
prompt = "Generate 4 highlights for the product based on the input. Each highlight should have a short text heading followed by a slightly longer explanation.\n\nInput: A high-quality smartphone with 64MP camera, 5G connectivity, and long battery life.\n\nHighlights:"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate output
model.eval()
outputs = model.generate(inputs['input_ids'], max_length=200, num_return_sequences=1)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)