Instructions to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF", filename="Umbra-v2.1-MoE-4x10.7-IQ2_XXS.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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Ollama:
ollama run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- Unsloth Studio
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-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 LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
- Lemonade
How to use LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/Umbra-v2.1-MoE-4x10.7-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Umbra-v2.1-MoE-4x10.7-GGUF-Q4_K_M
List all available models
lemonade list
Umbra-v2.1-MoE-4x10.7
The [Umbra Series] is an offshoot of the [Lumosia Series] With the goal to be a General assistant that has a knack for story telling and RP/ERP
-What's New in v2.1?
Umbra v2.1 isn't just a simple update; it's like giving the model a double shot of espresso. Ive changed the models and prompts, in an attempt to make Umbra not only your go-to assistant for general knowledge but also a great storyteller and RP/ERP companion.
-Longer Positive, Shorter Negative
In an effort to trick the gates into being less uptight, Ive added more positive prompts and snappier negative ones. These changes are based on the model's strengths and, frankly, my whimsical preferences.
-Experimental, As Always
Remember, folks, "v2.1" doesn't mean it's superior to its predecessors β it's just another step in the quest. It's the 'Empire Strikes Back' of our series β could be better, could be worse, but definitely more dramatic.
-Base Context and Coherence
Umbra v2.1 has a base context of 8k scrolling window.
-The Tavern Card
Just for fun - the Umbra Personality Tavern Card. It's your gateway to immersive storytelling experiences, a little like having a 'Choose Your Own Adventure' book, but way cooler because it's digital and doesn't get lost under your bed.
-Token Error? Fixed!
Umbra-v2 had a tokenizer error but was removed faster than you can say "Cops love Donuts"
So, give Umbra v2.1 a whirl and let me know how it goes. Your feedback is like the secret sauce in my development burger.
### System:
### USER:{prompt}
### Assistant:
Settings:
Temp: 1.0
min-p: 0.02-0.1
Evals:
- Avg: 73.59
- ARC: 69.11
- HellaSwag: 87.57
- MMLU: 66.48
- T-QA: 66.75
- Winogrande: 83.11
- GSM8K: 68.69
Examples:
posted soon
posted soon
π§© Configuration
base_model: vicgalle/CarbonBeagle-11B
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: vicgalle/CarbonBeagle-11B
positive_prompts: [Revamped]
- source_model: Sao10K/Fimbulvetr-10.7B-v1
positive_prompts: [Revamped]
- source_model: bn22/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED
positive_prompts: [Revamped]
- source_model: Yhyu13/LMCocktail-10.7B-v1
positive_prompts: [Revamed]
Umbra-v2-MoE-4x10.7 is a Mixure of Experts (MoE) made with the following models:
* [vicgalle/CarbonBeagle-11B](https://huggingface.co/vicgalle/CarbonBeagle-11B)
* [Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1)
* [bn22/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED](https://huggingface.co/bn22/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED)
* [Yhyu13/LMCocktail-10.7B-v1](https://huggingface.co/Yhyu13/LMCocktail-10.7B-v1)
π» Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Steelskull/Umbra-v2-MoE-4x10.7"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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