Instructions to use ruslandev/llama-3-70b-tagengo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ruslandev/llama-3-70b-tagengo-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ruslandev/llama-3-70b-tagengo-GGUF", dtype="auto")

llama-cpp-python

How to use ruslandev/llama-3-70b-tagengo-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ruslandev/llama-3-70b-tagengo-GGUF",
	filename="llama-3-70b-tagengo-unsloth.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ruslandev/llama-3-70b-tagengo-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ruslandev/llama-3-70b-tagengo-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 ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ruslandev/llama-3-70b-tagengo-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 ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ruslandev/llama-3-70b-tagengo-GGUF with Ollama:
```
ollama run hf.co/ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
```

Unsloth Studio

How to use ruslandev/llama-3-70b-tagengo-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 ruslandev/llama-3-70b-tagengo-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 ruslandev/llama-3-70b-tagengo-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ruslandev/llama-3-70b-tagengo-GGUF to start chatting

Docker Model Runner
How to use ruslandev/llama-3-70b-tagengo-GGUF with Docker Model Runner:
```
docker model run hf.co/ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M
```

Lemonade

How to use ruslandev/llama-3-70b-tagengo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ruslandev/llama-3-70b-tagengo-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-70b-tagengo-GGUF-Q4_K_M

List all available models

lemonade list

Uploaded model

Developed by: ruslandev
License: apache-2.0
Finetuned from model : unsloth/llama-3-70b-bnb-4bit

This model is finetuned on the Tagengo dataset. Please note - this model has been created for educational purposes and it needs further training/fine tuning.

How to use

The easiest way to use this model on your own computer is to use the GGUF version of this model (ruslandev/llama-3-70b-tagengo-GGUF) using a program such as llama.cpp. If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework gptchain.

git clone https://github.com/RuslanPeresy/gptchain.git
cd gptchain
pip install -r requirements-train.txt
python gptchain.py chat -m ruslandev/llama-3-70b-tagengo \
    --chatml true \
    -q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]'

Training

gptchain framework has been used for training.

python gptchain.py train -m unsloth/llama-3-70b-bnb-4bit \
    -dn tagengo_gpt4 \
    -sp checkpoints/llama-3-70b-tagengo \
    -hf llama-3-70b-tagengo \
    --max-steps 2400

Training hyperparameters

learning_rate: 2e-4
seed: 3407
gradient_accumulation_steps: 4
per_device_train_batch_size: 2
optimizer: adamw_8bit
lr_scheduler_type: linear
warmup_steps: 5
max_steps: 2400
weight_decay: 0.01

Training results

wandb report

2400 steps took 7 hours on a single H100

Downloads last month: 21

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

4-bit

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