| 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) | |