Instructions to use ndavidson/cisco_inam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ndavidson/cisco_inam with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("pansophic/rocket-3B")
model = PeftModel.from_pretrained(base_model, "ndavidson/cisco_inam")

llama-cpp-python

How to use ndavidson/cisco_inam with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ndavidson/cisco_inam",
	filename="cisco-inam.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ndavidson/cisco_inam with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ndavidson/cisco_inam
# Run inference directly in the terminal:
llama-cli -hf ndavidson/cisco_inam

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ndavidson/cisco_inam
# Run inference directly in the terminal:
llama-cli -hf ndavidson/cisco_inam

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ndavidson/cisco_inam
# Run inference directly in the terminal:
./llama-cli -hf ndavidson/cisco_inam

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ndavidson/cisco_inam
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ndavidson/cisco_inam

Use Docker

docker model run hf.co/ndavidson/cisco_inam

LM Studio
Jan
Ollama
How to use ndavidson/cisco_inam with Ollama:
```
ollama run hf.co/ndavidson/cisco_inam
```

Unsloth Studio new

How to use ndavidson/cisco_inam with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ndavidson/cisco_inam to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ndavidson/cisco_inam to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ndavidson/cisco_inam to start chatting

Docker Model Runner
How to use ndavidson/cisco_inam with Docker Model Runner:
```
docker model run hf.co/ndavidson/cisco_inam
```

Lemonade

How to use ndavidson/cisco_inam with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ndavidson/cisco_inam

Run and chat with the model

lemonade run user.cisco_inam-{{QUANT_TAG}}

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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]

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

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.7.2.dev0

Downloads last month: -

GGUF

Model size

3B params

Architecture

stablelm

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ndavidson/cisco_inam

Base model

stabilityai/stablelm-3b-4e1t

Adapter

(19)

this model

Paper for ndavidson/cisco_inam

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45