Instructions to use siacus/llama-2-7b-cap_verified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siacus/llama-2-7b-cap_verified with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="siacus/llama-2-7b-cap_verified",
	filename="llama-2-7b-cap_verified-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use siacus/llama-2-7b-cap_verified with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf siacus/llama-2-7b-cap_verified:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf siacus/llama-2-7b-cap_verified:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf siacus/llama-2-7b-cap_verified:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf siacus/llama-2-7b-cap_verified:Q4_K_M

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 siacus/llama-2-7b-cap_verified:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf siacus/llama-2-7b-cap_verified:Q4_K_M

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 siacus/llama-2-7b-cap_verified:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf siacus/llama-2-7b-cap_verified:Q4_K_M

Use Docker

docker model run hf.co/siacus/llama-2-7b-cap_verified:Q4_K_M

LM Studio
Jan
Ollama
How to use siacus/llama-2-7b-cap_verified with Ollama:
```
ollama run hf.co/siacus/llama-2-7b-cap_verified:Q4_K_M
```

Unsloth Studio new

How to use siacus/llama-2-7b-cap_verified 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 siacus/llama-2-7b-cap_verified 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 siacus/llama-2-7b-cap_verified to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for siacus/llama-2-7b-cap_verified to start chatting

Docker Model Runner
How to use siacus/llama-2-7b-cap_verified with Docker Model Runner:
```
docker model run hf.co/siacus/llama-2-7b-cap_verified:Q4_K_M
```

Lemonade

How to use siacus/llama-2-7b-cap_verified with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull siacus/llama-2-7b-cap_verified:Q4_K_M

Run and chat with the model

lemonade run user.llama-2-7b-cap_verified-Q4_K_M

List all available models

lemonade list

The data used to train the model are on Huggingface under siacus/cap_pe_verified

F16 version from merged weights created with llama.cpp on a CUDA GPU and the 4bit quantized version created on a Mac M2 Ultra Metal architecture. If you want to use the 4bit quantized version on CUDA, please quantize it directly from the F16 version.

For more information about this model refer the main repository for the supplementary material of the manuscript Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research.

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

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Model tree for siacus/llama-2-7b-cap_verified

Base model

meta-llama/Llama-2-7b-chat-hf

Quantized

(102)

this model

Dataset used to train siacus/llama-2-7b-cap_verified

Paper for siacus/llama-2-7b-cap_verified

Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research

Paper • 2411.00890 • Published Oct 31, 2024