Instructions to use QuantFactory/Fimbulvetr-11B-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Fimbulvetr-11B-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Fimbulvetr-11B-v2-GGUF",
	filename="Fimbulvetr-11B-v2.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Fimbulvetr-11B-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Fimbulvetr-11B-v2-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 QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Fimbulvetr-11B-v2-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 QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Fimbulvetr-11B-v2-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 QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Fimbulvetr-11B-v2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Fimbulvetr-11B-v2-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 QuantFactory/Fimbulvetr-11B-v2-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 QuantFactory/Fimbulvetr-11B-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Fimbulvetr-11B-v2-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Fimbulvetr-11B-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Fimbulvetr-11B-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Fimbulvetr-11B-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fimbulvetr-11B-v2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Fimbulvetr-11B-v2-GGUF

This is quantized version of Sao10K/Fimbulvetr-11B-v2 created using llama.cpp

Original Model Card

Cute girl to catch your attention.

https://huggingface.co/Sao10K/Fimbulvetr-11B-v2-GGUF <------ GGUF

Fimbulvetr-v2 - A Solar-Based Model

4/4 Status Update:

got a few reqs on wanting to support me: https://ko-fi.com/sao10k

anyway, status on v3 - Halted for time being, working on dataset work mainly. it's a pain, to be honest. the data I have isn't up to my standard for now. it's good, just not good enough

Prompt Formats - Alpaca or Vicuna. Either one works fine. Recommended SillyTavern Presets - Universal Light

Alpaca:

### Instruction:
<Prompt>
### Input:
<Insert Context Here>
### Response:

Vicuna:

System: <Prompt>

User: <Input>

Assistant:

Changelogs:

25/2 - repo renamed to remove test, model card redone. Model's officially out.
15/2 - Heavy testing complete. Good feedback.

Rant - Kept For Historical Reasons

Ramble to meet minimum length requirements:

Tbh i wonder if this shit is even worth doing. Like im just some broke guy lmao I've spent so much. And for what? I guess creds. Feels good when a model gets good feedback, but it seems like im invisible sometimes. I should be probably advertising myself and my models on other places but I rarely have the time to. Probably just internal jealousy sparking up here and now. Wahtever I guess.

Anyway cool EMT vocation I'm doing is cool except it pays peanuts, damn bruh 1.1k per month lmao. Government to broke to pay for shit. Pays the bills I suppose.

Anyway cool beans, I'm either going to continue the Solar Train or go to Mixtral / Yi when I get paid.

You still here?

Downloads last month: 109

GGUF

Model size

11B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support