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Tamnemtf
/
Ae-calem-mistral-7b-v0.1

Text Generation
Transformers
Safetensors
Vietnamese
mistral
NLP
Vietnamese
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
Ae-calem-mistral-7b-v0.1
File size: 4,388 Bytes 
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---
library_name: transformers
tags: []
---

# Model Card for Model ID

Provide information - Chatbots can be programmed with a large knowledge base to answer users' questions on a variety of topics. They can provide facts, data, explanations, definitions, etc.
Complete tasks - Chatbots can be integrated with other systems and APIs to actually do things for users, like placing an order, booking a flight, scheduling a meeting, etc.
Offer recommendations - Based on a user's preferences and past interactions, chatbots can suggest products, services, content and more that might be relevant and useful to the user.
Provide customer service - Chatbots can handle many simple customer service interactions to answer questions, handle complaints, process returns, etc. This allows human agents to focus on more complex issues.
Generate conversational responses - Using NLP and machine learning, chatbots can understand natural language and generate conversational responses, creating fluent interactions.
Provide company persona - Chatbots can take on a specific personality and tone of voice to personify a brand and create more natural conversations with customers.



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
- **Model type:** Llama2
- **Language(s) (NLP):** Vietnamese
- **License:** llama2
- **Finetuned from model Viet-Mistral/Vistral-7B-Chat:** [More Information Needed]


### Training Data

Our dataset was make base on our university sudent notebook. It includes majors, university regulations and other information about our university.  
[Tamnemtf/llama2_hcmue_1]

### 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]

[More Information Needed]


#### 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. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### 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

[More Information Needed]

### 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. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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