Instructions to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF",
	filename="L3.1-Celestial-Stone-2x8B.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-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/L3.1-Celestial-Stone-2x8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3.1-Celestial-Stone-2x8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3.1-Celestial-Stone-2x8B-GGUF

This is quantized version of v000000/L3.1-Celestial-Stone-2x8B created using llama.cpp

Original Model Card

Content:
This models output's can be a bit unhinged.

Llama-3.1-Celestial-Stone-2x8B (BF16)

Mixture of Experts (14B).

Both experts are used in tandem when generating a token.

Llama.CPP - GGUF.

Thank you mradermacher for the quants!

----> GGUF iMatrix

----> GGUF static

Other alternative quants:

----> Q8_0 GGUF by dasChronos1

----> Q6_K GGUF

----> Q4_K_M GGUF by aashish1904

----> Q2_K GGUF by aashish1904

The first expert is Instruct 405B distillation/RP vector merge (Supernova-Lite, Niitama1.1, Storm)

The second expert is ERP/Reddit data merge (Celeste1.5, Stheno3.4, Storm)

The base model is Sao10k/L3.1-Stheno-3.4 with the Sunfall LoRa 0.6.1 to make it understand SillyTavern prompts and storywriting better.

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

Other Details:

The model has 131072 context length, and is on Llama-3.1 and Mixtral architecture.

I did not abliterate the base model at all, so it will refuse zero-shot unethical questions. I recommend avoiding keywords like 'assistant, helpful, kind'

Recipe (I'm sorry...):

slices:
  - sources:
      - model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: nearswap
base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
  t:
    - value: 0.0001
dtype: bfloat16
out_type: float16
slices:
  - sources:
      - model: v000000/Llama-3.1-8B-Stheno-v3.4-abliterated
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: slerp
base_model: v000000/Llama-3.1-8B-Stheno-v3.4-abliterated
parameters:
  t:
    - filter: self_attn
      value: [0.1, 0.6, 0.3, 0.8, 0.5]
    - filter: mlp
      value: [0.9, 0.4, 0.7, 0.2, 0.5]
    - value: 0.5
dtype: float32
models:
  - model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      weight: 1.0
  - model: v000000/L3.1-Niitorm-8B-t0.0001
    parameters:
      weight: 0.4
merge_method: task_arithmetic
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
    normalize: false
dtype: float16
models:
  - model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      weight: 0.0
  - model: v000000/L3.1-Niitorm-8B-t0.0001
    parameters:
      weight: 1.25
merge_method: task_arithmetic
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
    normalize: false
dtype: float16
models:
  - model: v000000/L3.1-8B-RP-Test-003-Task_Arithmetic
merge_method: slerp
base_model: v000000/L3.1-8B-RP-Test-002-Task_Arithmetic+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
  t:
    - value: [0, 0, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0, 0]
dtype: float16
base_model: nothingiisreal/L3.1-8B-Celeste-V1.5+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: nothingiisreal/L3.1-8B-Celeste-V1.5+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
    parameters:
      weight: 0.7
  - layer_range: [0, 32]
    model: v000000/L3.1-Sthenorm-8B
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: nothingiisreal/L3.1-8B-Celeste-V1.5
    parameters:
      weight: 0.2
base_model: crestf411/L3.1-8B-sunfall-stheno-v0.6.1
experts_per_token: 2
local_experts: 2
gate_mode: random
dtype: bfloat16
experts:
  - source_model: v000000/L3.1-Storniitova-8B
  - source_model: x0000001/l3.1-part_aaa