Instructions to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit",
	filename="unsloth.Q8_0.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 Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

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 Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

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 Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

Use Docker

docker model run hf.co/Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

LM Studio
Jan

vLLM

How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

Ollama
How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with Ollama:
```
ollama run hf.co/Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
```

Unsloth Studio

How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit 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 Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit 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 Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit to start chatting

Docker Model Runner
How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with Docker Model Runner:
```
docker model run hf.co/Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0
```

Lemonade

How to use Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit:Q8_0

Run and chat with the model

lemonade run user.Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit-Q8_0

List all available models

lemonade list

Tinytron-ORCA-7B-Instruct_CODE_Python-GGUF_Spanish_English_8bit / tokenizer.model

Agnuxo

Upload 11 files

8797440 verified almost 2 years ago

download

history blame contribute delete

500 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 846d1e9f6058b85dd52ed2060eebbf7be162acc365818e037d9e301b68518103
Size of remote file:: 500 kB
SHA256:: bf76be6426735543a444864193f87669a157ca0c18fe74522e0414d340f49dee

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.