Instructions to use cklam12345/jarvis_llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cklam12345/jarvis_llama with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cklam12345/jarvis_llama",
	filename="jarvis.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cklam12345/jarvis_llama with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cklam12345/jarvis_llama
# Run inference directly in the terminal:
llama-cli -hf cklam12345/jarvis_llama

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cklam12345/jarvis_llama
# Run inference directly in the terminal:
llama-cli -hf cklam12345/jarvis_llama

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 cklam12345/jarvis_llama
# Run inference directly in the terminal:
./llama-cli -hf cklam12345/jarvis_llama

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 cklam12345/jarvis_llama
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cklam12345/jarvis_llama

Use Docker

docker model run hf.co/cklam12345/jarvis_llama

LM Studio
Jan
Ollama
How to use cklam12345/jarvis_llama with Ollama:
```
ollama run hf.co/cklam12345/jarvis_llama
```

Unsloth Studio

How to use cklam12345/jarvis_llama 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 cklam12345/jarvis_llama 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 cklam12345/jarvis_llama to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cklam12345/jarvis_llama to start chatting

Docker Model Runner
How to use cklam12345/jarvis_llama with Docker Model Runner:
```
docker model run hf.co/cklam12345/jarvis_llama
```

Lemonade

How to use cklam12345/jarvis_llama with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cklam12345/jarvis_llama

Run and chat with the model

lemonade run user.jarvis_llama-{{QUANT_TAG}}

List all available models

lemonade list

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Developed by: [cklam]
Funded by [optional]: [patho.ai]
Shared by [optional]: [cklam]
Model type: [llama 7B fined tuned with jarvis datasets]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [llama 7B]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [https://github.com/cklam12345/jarvis_llama]

Uses

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: [AMD GPU/FPGA preferred, Nvidia H100 overheated after 2 hours training]
Hours used: [300]
Cloud Provider: [patho.ai]
Compute Region: [USA]
Carbon Emitted: [100% Solar Farmed Energy]

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]

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More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Downloads last month: 51

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

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Paper for cklam12345/jarvis_llama

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 50