Instructions to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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 QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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 QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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 QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-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 QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF

This is quantized version of kimhyeongjun/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor created using llama.cpp

Original Model Card

kimhyeongjun/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor

This is my personal toy project for Chuseok(Korean Thanksgiving Day).

This model is a fine-tuned version of NousResearch/Hermes-3-Llama-3.1-8B on the Korean_synthetic_financial_dataset_21K.

Model description

Everything happened automatically without any user intervention.

Based on finance PDF data collected directly from the web, we refined the raw data using the 'meta-llama/Meta-Llama-3.1-70B-Instruct-FP8' model. After generating synthetic data based on the cleaned data, we further evaluated the quality of the generated data using the 'meta-llama/Llama-Guard-3-8B' and 'RLHFlow/ArmoRM-Llama3-8B-v0.1' models. We then used 'Alibaba-NLP/gte-large-en-v1.5' to extract embeddings and applied Faiss to perform Jaccard distance-based nearest neighbor analysis to construct the final dataset of 21k, which is diverse and sophisticated.

모든 과정은 사용자의 개입 없이 자동으로 진행되었습니다.

웹에서 직접 수집한 금융 관련 PDF 데이터를 기반으로, 돈이 없어서 'meta-llama/Meta-Llama-3.1-70B-Instruct-FP8' 모델을 활용하여 Raw 데이터를 정제하였습니다. 정제된 데이터를 바탕으로 합성 데이터를 생성한 후, 'meta-llama/Llama-Guard-3-8B' 및 'RLHFlow/ArmoRM-Llama3-8B-v0.1' 모델을 통해 생성된 데이터의 품질을 심층적으로 평가하였습니다. 이어서 'Alibaba-NLP/gte-large-en-v1.5'를 사용하여 임베딩을 추출하고, Faiss를 적용하여 자카드 거리 기반의 근접 이웃 분석을 수행함으로써 다양하고 정교한 최종 데이터셋 21k을 직접 구성하였습니다.

Task duration

3days (20240914~20240916)

evaluation

Nothing (I had to take the Thanksgiving holiday off.)

sample

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 90

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Hermes-3-Llama-3.1-8B-Kor-Finance-Advisor-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

NousResearch/Hermes-3-Llama-3.1-8B

Quantized

(51)

this model