Instructions to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF",
	filename="mlabonne-TwinLlama-3.1-8B-DPO-IQ4_XS.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 featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-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 featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-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 featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M

Use Docker

docker model run hf.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M

Ollama
How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with Ollama:
```
ollama run hf.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
```

Unsloth Studio

How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-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 featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-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 featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with Docker Model Runner:
```
docker model run hf.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M
```

Lemonade

How to use featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mlabonne-TwinLlama-3.1-8B-DPO-GGUF-Q4_K_M

List all available models

lemonade list

m8than commited on Nov 7, 2024

Commit

3844ded

verified ·

1 Parent(s): df12010

Upload folder using huggingface_hub

Browse files

Files changed (14) hide show

.gitattributes +12 -0
README.md +47 -0
featherless-quants.png +3 -0
mlabonne-TwinLlama-3.1-8B-DPO-IQ4_XS.gguf +3 -0
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README.md ADDED Viewed

	@@ -0,0 +1,47 @@

+---
+base_model: mlabonne-TwinLlama-3.1-8B-DPO
+pipeline_tag: text-generation
+quantized_by: featherless-ai-quants
+---
+# mlabonne-TwinLlama-3.1-8B-DPO GGUF Quantizations 🚀
+![Featherless AI Quants](./featherless-quants.png)
+*Optimized GGUF quantization files for enhanced model performance*
+> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
+---
+## Available Quantizations 📊
+| Quantization Type | File | Size |
+|-------------------|------|------|
+| IQ4_XS | [mlabonne-TwinLlama-3.1-8B-DPO-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-IQ4_XS.gguf) | 4276.62 MB |
+| Q2_K | [mlabonne-TwinLlama-3.1-8B-DPO-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q2_K.gguf) | 3031.86 MB |
+| Q3_K_L | [mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_L.gguf) | 4121.74 MB |
+| Q3_K_M | [mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_M.gguf) | 3832.74 MB |
+| Q3_K_S | [mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q3_K_S.gguf) | 3494.74 MB |
+| Q4_K_M | [mlabonne-TwinLlama-3.1-8B-DPO-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q4_K_M.gguf) | 4692.78 MB |
+| Q4_K_S | [mlabonne-TwinLlama-3.1-8B-DPO-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q4_K_S.gguf) | 4475.28 MB |
+| Q5_K_M | [mlabonne-TwinLlama-3.1-8B-DPO-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q5_K_M.gguf) | 5467.40 MB |
+| Q5_K_S | [mlabonne-TwinLlama-3.1-8B-DPO-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q5_K_S.gguf) | 5339.90 MB |
+| Q6_K | [mlabonne-TwinLlama-3.1-8B-DPO-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q6_K.gguf) | 6290.44 MB |
+| Q8_0 | [mlabonne-TwinLlama-3.1-8B-DPO-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-TwinLlama-3.1-8B-DPO-GGUF/blob/main/mlabonne-TwinLlama-3.1-8B-DPO-Q8_0.gguf) | 8145.11 MB |
+---
+## ⚡ Powered by [Featherless AI](https://featherless.ai)
+### Key Features
+- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
+- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
+- 📚 **Vast Compatibility** - Support for 2400+ models and counting
+- 💎 **Affordable Pricing** - Starting at just $10/month
+---
+**Links:**
+[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

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