Instructions to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Llama3-ChatQA-1.5-70B-GGUF",
	filename="Llama3-ChatQA-1.5-70B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

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 tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

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 tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/Llama3-ChatQA-1.5-70B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Llama3-ChatQA-1.5-70B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

Ollama
How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/Llama3-ChatQA-1.5-70B-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 tensorblock/Llama3-ChatQA-1.5-70B-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 tensorblock/Llama3-ChatQA-1.5-70B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Llama3-ChatQA-1.5-70B-GGUF to start chatting

Docker Model Runner
How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Llama3-ChatQA-1.5-70B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Llama3-ChatQA-1.5-70B-GGUF:Q2_K

Run and chat with the model

lemonade run user.Llama3-ChatQA-1.5-70B-GGUF-Q2_K

List all available models

lemonade list

nvidia/Llama3-ChatQA-1.5-70B - GGUF

This repo contains GGUF format model files for nvidia/Llama3-ChatQA-1.5-70B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

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## Prompt template

<|begin_of_text|>System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.

User: {prompt}

Assistant:

Model file specification

Filename	Quant type	File Size	Description
Llama3-ChatQA-1.5-70B-Q2_K.gguf	Q2_K	26.375 GB	smallest, significant quality loss - not recommended for most purposes
Llama3-ChatQA-1.5-70B-Q3_K_S.gguf	Q3_K_S	30.912 GB	very small, high quality loss
Llama3-ChatQA-1.5-70B-Q3_K_M.gguf	Q3_K_M	34.267 GB	very small, high quality loss
Llama3-ChatQA-1.5-70B-Q3_K_L.gguf	Q3_K_L	37.141 GB	small, substantial quality loss
Llama3-ChatQA-1.5-70B-Q4_0.gguf	Q4_0	39.970 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama3-ChatQA-1.5-70B-Q4_K_S.gguf	Q4_K_S	40.347 GB	small, greater quality loss
Llama3-ChatQA-1.5-70B-Q4_K_M.gguf	Q4_K_M	42.520 GB	medium, balanced quality - recommended
Llama3-ChatQA-1.5-70B-Q5_0.gguf	Q5_0	48.657 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama3-ChatQA-1.5-70B-Q5_K_S.gguf	Q5_K_S	48.657 GB	large, low quality loss - recommended
Llama3-ChatQA-1.5-70B-Q5_K_M.gguf	Q5_K_M	49.950 GB	large, very low quality loss - recommended
Llama3-ChatQA-1.5-70B-Q8_0	Q6_K	74.975 GB	very large, extremely low quality loss
Llama3-ChatQA-1.5-70B-Q6_K	Q8_0	57.888 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Llama3-ChatQA-1.5-70B-GGUF --include "Llama3-ChatQA-1.5-70B-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Llama3-ChatQA-1.5-70B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 32

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

Model tree for tensorblock/Llama3-ChatQA-1.5-70B-GGUF

Base model

nvidia/Llama3-ChatQA-1.5-70B

Quantized

(5)

this model