Model Card for Mixtral-Finetuned
This is a fine-tuned version of the Mixtral model, designed for text-generation tasks. It has been fine-tuned to assist with travel industry connections, specifically for connecting clients with suppliers.
Model Details
Model Description
This model is a fine-tuned version of the Mixtral model, optimized for generating responses in the context of travel industry partnerships. It is designed to assist users by providing accurate and contextually relevant responses.
- Developed by: [Your Name or Organization]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [Your Name or Organization]
- Model type: Text Generation
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: mistralai/Mixtral-8x7B-v0.1
Model Sources [optional]
- Repository: [Link to your GitHub repository, if applicable]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [Link to a live demo, if available]
Uses
Direct Use
This model is intended for direct use in text-generation tasks, specifically for generating responses related to travel industry partnerships.
Downstream Use [optional]
This model can be fine-tuned further for specific downstream tasks, such as customer support or travel itinerary planning.
Out-of-Scope Use
This model is not intended for tasks outside the scope of text generation, such as image processing or audio analysis.
Bias, Risks, and Limitations
This model may inherit biases present in the training data. Users should be cautious when using it in sensitive contexts.
Recommendations
Users should evaluate the model's outputs carefully, especially in high-stakes applications.
How to Get Started with the Model
Use the code below to get started with the model:
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("obinju/mixtral-finetuned")
4tokenizer = AutoTokenizer.from_pretrained("obinju/mixtral-finetuned")
5
6prompt = "Who are you?"
7inputs = tokenizer(prompt, return_tensors="pt")
8outputs = model.generate(**inputs, max_length=50)
9response = tokenizer.decode(outputs[0], skip_special_tokens=True)
10
11print(response)
