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 Settings

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

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

siacus commited on Nov 18, 2024

Commit

3352d85

verified ·

1 Parent(s): 21ecb2d

Create README.md

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+---
+license: mit
+datasets:
+- siacus/cap_pe_verified
+base_model:
+- meta-llama/Llama-2-7b-chat-hf
+new_version: siacus/llama-2-7b-cap_verified
+---
+The data used to train the model are on Huggingface under [siacus/cap_pe_verified](https://huggingface.co/datasets/siacus/cap_pe_verified)
+F16 version from merged weights created with [llama.cpp](https://github.com/ggerganov/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](https://github.com/siacus/rethinking-scale) for the supplementary material of the manuscript [Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research](https://arxiv.org/abs/2411.00890).