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
Base Model Repository: Mistral 7B v0.3 Quantized
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

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)