Instructions to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF",
	filename="dolphin3.0-llama3.2-1b-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF: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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF: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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF with Ollama:
```
ollama run hf.co/WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_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 WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0
```

Lemonade

How to use WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Dolphin3.0-Llama3.2-1B-Q8_0-GGUF-Q8_0

List all available models

lemonade list

Dolphin3.0-Llama3.2-1B-Q8_0-GGUF / README.md

WSDW

Upload README.md with huggingface_hub

f6f1748 verified about 1 year ago

preview code

Raw

History Blame

2.29 kB

	---
	base_model: cognitivecomputations/Dolphin3.0-Llama3.2-1B
	datasets:
	- OpenCoder-LLM/opc-sft-stage1
	- OpenCoder-LLM/opc-sft-stage2
	- microsoft/orca-agentinstruct-1M-v1
	- microsoft/orca-math-word-problems-200k
	- NousResearch/hermes-function-calling-v1
	- AI-MO/NuminaMath-CoT
	- AI-MO/NuminaMath-TIR
	- allenai/tulu-3-sft-mixture
	- cognitivecomputations/dolphin-coder
	- HuggingFaceTB/smoltalk
	- cognitivecomputations/samantha-data
	- m-a-p/CodeFeedback-Filtered-Instruction
	- m-a-p/Code-Feedback
	language:
	- en
	license: llama3.2
	tags:
	- llama-cpp
	- gguf-my-repo
	---

	# WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF
	This model was converted to GGUF format from [`cognitivecomputations/Dolphin3.0-Llama3.2-1B`](https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.2-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.2-1B) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF --hf-file dolphin3.0-llama3.2-1b-q8_0.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF --hf-file dolphin3.0-llama3.2-1b-q8_0.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF --hf-file dolphin3.0-llama3.2-1b-q8_0.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo WSDW/Dolphin3.0-Llama3.2-1B-Q8_0-GGUF --hf-file dolphin3.0-llama3.2-1b-q8_0.gguf -c 2048
	```