Instructions to use koesn/Mistral-7B-Sunda-v1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koesn/Mistral-7B-Sunda-v1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koesn/Mistral-7B-Sunda-v1.0-GGUF",
	filename="mistral-7b-sunda-v1.0.IQ3_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 koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use koesn/Mistral-7B-Sunda-v1.0-GGUF with Ollama:
```
ollama run hf.co/koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
```

Unsloth Studio

How to use koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-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 koesn/Mistral-7B-Sunda-v1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for koesn/Mistral-7B-Sunda-v1.0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use koesn/Mistral-7B-Sunda-v1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M
```

Lemonade

How to use koesn/Mistral-7B-Sunda-v1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koesn/Mistral-7B-Sunda-v1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Sunda-v1.0-GGUF-Q4_K_M

List all available models

lemonade list

koesn commited on Mar 10, 2024

Commit

08f3a9f

verified ·

1 Parent(s): 6d4ff14

Update README.md

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README.md CHANGED Viewed

@@ -1,9 +1,9 @@
 ---
 license: apache-2.0
 language:
 - en
 - id
-- su
 ---
 # Mistral-7B-Sunda-v1.0-GGUF
@@ -38,13 +38,6 @@ not available
 # Original Model Card
----
-license: apache-2.0
-language:
-- su
-- en
-- id
----
 This is a fine tune of Mistral-7B-v0.1 on a very limited range of Sundanese language datasets that are available.
 This is a learning project for me where I just wanted to see if it's possible to teach a model a new language that it does not inherently support with just a QLora fine tune. It won't only speak sundanese but it just adds sundanese capability to the model that is to me impressive for the limited data and short amount of training time.

 ---
 license: apache-2.0
 language:
+- su
 - en
 - id
 ---
 # Mistral-7B-Sunda-v1.0-GGUF
 # Original Model Card
 This is a fine tune of Mistral-7B-v0.1 on a very limited range of Sundanese language datasets that are available.
 This is a learning project for me where I just wanted to see if it's possible to teach a model a new language that it does not inherently support with just a QLora fine tune. It won't only speak sundanese but it just adds sundanese capability to the model that is to me impressive for the limited data and short amount of training time.