---
model-index:
- name: cisco-inam
  results: []
language:
- en
base_model: stabilityai/stablelm-3b-4e1t
datasets:
- ndavidson/cisco_datacenter_rag
library_name: peft
tags:
- networking
---

# Cisco iNAM

Cisco iNAM (Intelligent Networking, Automation, and Management), is a nano sized LLM used for asking questions about Cisco Datacenter Products.


## Model Details

### Model Description

Model is quantized to 4-bit to be able to run inference on physical deployments of datacenter products. Initial launch is planned for Nexus Dashboard.

- **Developed by:** Cisco
- **Funded by [optional]:** Cisco
- **Model type:** Transformer
- **Language(s) (NLP):** English
- **License:** Cisco Commercial


### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Input Format
The model is trained with the ChatML format:

```
<|im_start|>system
System message here.<|im_end|>
<|im_start|>user
Your message here!<|im_end|>
<|im_start|>assistant
```

### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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## Bias, Risks, and Limitations

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### Recommendations

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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

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### Training Procedure 

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#### 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]

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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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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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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. -->

**BibTeX:**

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**APA:**

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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 0.7.2.dev0