Instructions to use ruslandev/llama-3-8b-gpt-4o-ru1.0-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 ruslandev/llama-3-8b-gpt-4o-ru1.0-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 ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ruslandev/llama-3-8b-gpt-4o-ru1.0-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 ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ruslandev/llama-3-8b-gpt-4o-ru1.0-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-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ruslandev/llama-3-8b-gpt-4o-ru1.0-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-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M

Use Docker

docker model run hf.co/ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf with Ollama:
```
ollama run hf.co/ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
```

Unsloth Studio

How to use ruslandev/llama-3-8b-gpt-4o-ru1.0-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-3-8b-gpt-4o-ru1.0-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-3-8b-gpt-4o-ru1.0-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-3-8b-gpt-4o-ru1.0-gguf to start chatting

Docker Model Runner
How to use ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf with Docker Model Runner:
```
docker model run hf.co/ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M
```

Lemonade

How to use ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-8b-gpt-4o-ru1.0-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

ruslandev commited on Jun 30, 2024

Commit

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@@ -18,7 +18,7 @@ datasets:
 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
 The idea behind this model is to train on a dataset derived from a smaller subset of the [tagengo-gpt4](https://huggingface.co/datasets/lightblue/tagengo-gpt4), but with improved data quality.
 I tried to achieve higher data quality by prompting GPT-4o, the latest OpenAI's LLM with better multilingual capabilities. The training objective is primarily focused on the Russian language (80% of the training examples).
-The model shows promising results on the MT-Bench evaluation benchmark, surpassing GPT-3.5-turbo and being on par with [Suzume](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) in Russian language scores,
 even though the latter is trained on 8x bigger and more diverse dataset.
 ## How to use

 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
 The idea behind this model is to train on a dataset derived from a smaller subset of the [tagengo-gpt4](https://huggingface.co/datasets/lightblue/tagengo-gpt4), but with improved data quality.
 I tried to achieve higher data quality by prompting GPT-4o, the latest OpenAI's LLM with better multilingual capabilities. The training objective is primarily focused on the Russian language (80% of the training examples).
+After training for 1 epoch on 2 NVIDIA A100 the model shows promising results on the MT-Bench evaluation benchmark, surpassing GPT-3.5-turbo and being on par with [Suzume](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) in Russian language scores,
 even though the latter is trained on 8x bigger and more diverse dataset.
 ## How to use