Instructions to use afrideva/llama2_xs_460M_uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use afrideva/llama2_xs_460M_uncensored-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/llama2_xs_460M_uncensored-GGUF: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 afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/llama2_xs_460M_uncensored-GGUF: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 afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/llama2_xs_460M_uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/llama2_xs_460M_uncensored-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/llama2_xs_460M_uncensored-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M

Ollama
How to use afrideva/llama2_xs_460M_uncensored-GGUF with Ollama:
```
ollama run hf.co/afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/llama2_xs_460M_uncensored-GGUF 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 afrideva/llama2_xs_460M_uncensored-GGUF 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 afrideva/llama2_xs_460M_uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/llama2_xs_460M_uncensored-GGUF to start chatting

Docker Model Runner
How to use afrideva/llama2_xs_460M_uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/llama2_xs_460M_uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/llama2_xs_460M_uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama2_xs_460M_uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

haramberesearch/llama2_xs_460M_uncensored-GGUF

Quantized GGUF model files for llama2_xs_460M_uncensored from haramberesearch

Name	Quant method	Size
llama2_xs_460m_uncensored.fp16.gguf	fp16	925.45 MB
llama2_xs_460m_uncensored.q2_k.gguf	q2_k	212.56 MB
llama2_xs_460m_uncensored.q3_k_m.gguf	q3_k_m	238.87 MB
llama2_xs_460m_uncensored.q4_k_m.gguf	q4_k_m	288.52 MB
llama2_xs_460m_uncensored.q5_k_m.gguf	q5_k_m	333.29 MB
llama2_xs_460m_uncensored.q6_k.gguf	q6_k	380.87 MB
llama2_xs_460m_uncensored.q8_0.gguf	q8_0	492.67 MB

Original Model Card:

llama2_xs_460M_uncensored

Model Details

llama2_xs_460M_experimental DPO finedtuned to remove alignment (3 epochs QLoRa).

Model Description

Developed by: Harambe Research
Model type: llama2
Finetuned from model: llama2_xs_460M_experimental

Out-of-Scope Use

Don't use this to do bad things. Bad things are bad.

Recommendations

Users (both direct and downstream) should be aware of the risks, biases and limitations of the model.

How to Get Started with the Model

https://github.com/oobabooga/text-generation-webui

Downloads last month: 236

GGUF

Model size

0.5B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/llama2_xs_460M_uncensored-GGUF

Base model

haramberesearch/llama2_xs_460M_uncensored

Quantized

(1)

this model

afrideva
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llama2_xs_460M_uncensored-GGUF