Instructions to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-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 QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-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 QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF / README.md

aashish1904

Upload README.md with huggingface_hub

f26a0f7 verified about 2 years ago

preview code

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metadata

base_model: unsloth/Meta-Llama-3.1-8B
language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - sft

QuantFactory/Name-Classifier_Llama3-8B-16b_V2.2.1-GGUF

This is quantized version of yaamin6236/Name-Classifier_Llama3-8B-16b_V2.2.1 created using llama.cpp

Original Model Card

Uploaded model

Developed by: yaamin6236
License: apache-2.0
Finetuned from model : unsloth/Meta-Llama-3.1-8B

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.