Instructions to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("issai/LLama-3.1-KazLLM-1.0-8B-GGUF4", dtype="auto")

llama-cpp-python

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="issai/LLama-3.1-KazLLM-1.0-8B-GGUF4",
	filename="checkpoints_llama8b_031224_18900-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4: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 issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4: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 issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Use Docker

docker model run hf.co/issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

LM Studio
Jan
Ollama
How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Ollama:
```
ollama run hf.co/issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
```

Unsloth Studio

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 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 issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 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 issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 to start chatting

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Docker Model Runner:
```
docker model run hf.co/issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M
```

Lemonade

How to use issai/LLama-3.1-KazLLM-1.0-8B-GGUF4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull issai/LLama-3.1-KazLLM-1.0-8B-GGUF4:Q4_K_M

Run and chat with the model

lemonade run user.LLama-3.1-KazLLM-1.0-8B-GGUF4-Q4_K_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By accessing this model, you are agreeing to the LLama 3.1 terms and cc-by-nc license for non commnercial use

Model Overview

Made in Kazakhstan - Қазақстанда жасалған

Description:

LLama-3.1-KazLLM-1.0-8B-GGUF4 is a quantized version of issai/LLama-3.1-KazLLM-1.0-8B large language model.

Terms of use

By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the following:

Additionally, this model is licensed under the CC BY-NC 4.0 license.

Usage:

You can also run KazLLM like that using vllm.

1 cell.

# Setup env: 
!conda create -n vllm_test python=3.10 -y
!pip install vllm==0.6.3
!pip install ipykernel
!python -m ipykernel install --user --name vllm_test

2 cell

# load model
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from vllm import LLM, SamplingParams

# In this script, we demonstrate how to pass input to the chat method:
conversation = [
   {
      "role": "system",
      "content": "You are a helpful assistant"
   },
   {
      "role": "user",
      "content": "Hello"
   },
   {
      "role": "assistant",
      "content": "Hello! How can I assist you today?"
   },
   {
      "role": "user",
      "content": "Write an essay about the importance of higher education.",
   },
]

# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="checkpoints_llama8b_031224_18900-Q4_K_M.gguf",
         gpu_memory_utilization=0.95, max_model_len=32000)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.chat(conversation, sampling_params)

3 cell

# Print the outputs.
for output in outputs:
   prompt = output.prompt
   generated_text = output.outputs[0].text
   print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
   
for output in outputs:
   prompt = output.prompt
   generated_text = output.outputs[0].text
   print(f"Prompt: {prompt}, Generated text: {generated_text}")

Or you can also run using llama.cpp if you want, because vllm not yet fully optimized for gguf.

Input:

Input Type(s): Text
Input Format: String
Input Parameters: One Dimensional (1D)
Other Properties Related to Input: Max of 128k tokens

Output:

Output Type(s): Text
Output Format: String
Output Parameters: One Dimensional (1D)
Other Properties Related to Output: Max of 4k tokens

Model Version:

v1.0

Ethical and Legal Considerations:

The models provided in this repository, including ISSAI KAZ-LLM, are powerful tools designed to advance research and innovation. However, it is essential to use these models responsibly, ethically, and in accordance with applicable laws and regulations.

Key Guidelines for Responsible Use:

Bias and Fairness: While the models are designed to reflect linguistic and cultural diversity, they may still exhibit biases. Please ensure that the outputs are evaluated critically and not used to perpetuate harmful stereotypes or unfair practices.
Content Generation: Generated content should not be used to produce harmful, misleading, or deceptive information. Users should take extra care in ensuring the authenticity and reliability of the output in all contexts.
Privacy and Data Protection: Ensure that any personal data input into the models complies with privacy laws and regulations. Do not use the models to generate or process sensitive personal information unless proper safeguards are in place.
Ethical Considerations: The models should not be used to create content that promotes violence, hatred, discrimination, or illegal activities. Always adhere to ethical standards and foster positive impact through AI technologies.
Accountability: The responsibility for the use of the models lies with the users. We encourage you to evaluate the generated content critically and consider the potential social, cultural, and ethical consequences of its use.

By accessing or using these models, you agree to follow these guidelines and contribute to the responsible development and application of AI technologies.

For any questions or concerns, please contact us at issai@nu.edu.kz.

Downloads last month: 33

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Collection including issai/LLama-3.1-KazLLM-1.0-8B-GGUF4

ISSAI-KazLLM-1.0

Collection

7 items • Updated Dec 10, 2024 • 38