Instructions to use mradermacher/Swallow-70b-NVE-RP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Swallow-70b-NVE-RP-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Swallow-70b-NVE-RP-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/Swallow-70b-NVE-RP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/Swallow-70b-NVE-RP-GGUF",
	filename="Swallow-70b-NVE-RP.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 mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Swallow-70b-NVE-RP-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-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 mradermacher/Swallow-70b-NVE-RP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Swallow-70b-NVE-RP-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/Swallow-70b-NVE-RP-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Swallow-70b-NVE-RP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Swallow-70b-NVE-RP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Swallow-70b-NVE-RP-GGUF-Q4_K_M

List all available models

lemonade list

mradermacher commited on May 6, 2024

Commit

40ce832

verified ·

1 Parent(s): 0934099

auto-patch README.md

Browse files

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README.md +1 -1

README.md CHANGED Viewed

@@ -2,6 +2,7 @@
 base_model: nitky/Swallow-70b-NVE-RP
 language:
 - en
 library_name: transformers
 license: llama2
 model_type: llama
@@ -47,7 +48,6 @@ more details, including on how to concatenate multi-part files.
 | [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
 | [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 base_model: nitky/Swallow-70b-NVE-RP
 language:
 - en
+- ja
 library_name: transformers
 license: llama2
 model_type: llama
 | [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
 | [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF/resolve/main/Swallow-70b-NVE-RP.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):