coeusk
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quantized-mistral-finetuned

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coeusk commited on Jan 11, 2025

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----
 base_model: unsloth/mistral-7b-v0.3-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-- PEFT 0.14.0

 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)