Instructions to use saishf/Fett-Eris-Mix-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Fett-Eris-Mix-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="saishf/Fett-Eris-Mix-7B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saishf/Fett-Eris-Mix-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saishf/Fett-Eris-Mix-7B-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 saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saishf/Fett-Eris-Mix-7B-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 saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saishf/Fett-Eris-Mix-7B-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 saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use saishf/Fett-Eris-Mix-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saishf/Fett-Eris-Mix-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishf/Fett-Eris-Mix-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M

SGLang

How to use saishf/Fett-Eris-Mix-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "saishf/Fett-Eris-Mix-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishf/Fett-Eris-Mix-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "saishf/Fett-Eris-Mix-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishf/Fett-Eris-Mix-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use saishf/Fett-Eris-Mix-7B-GGUF with Ollama:
```
ollama run hf.co/saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use saishf/Fett-Eris-Mix-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M
```

Lemonade

How to use saishf/Fett-Eris-Mix-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saishf/Fett-Eris-Mix-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fett-Eris-Mix-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

This model is an attempt at making a smart rp model with the finesse of Epiculous/Fett-uccine-7B.
From limited testing i've found it to be my favourite of my personal 7B models. It stays pretty coherent at 8k+ ctx.
I like to use "Alpaca" format with "Universal-Light" for longer messages. Switching to ChatML causes the messages to be much shorter? I haven't a clue why but sometimes it's nice.
It doesn't seem to show many issues but i'd be willing to try to fix any problems or bugs as it shows some potential.

Merge Method

This model was merged using the DARE TIES merge method using OpenPipe/mistral-ft-optimized-1227 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: OpenPipe/mistral-ft-optimized-1227
    # No parameters necessary for base model
  - model: Epiculous/Fett-uccine-7B
    parameters:
      density: 0.53
      weight: 0.4
  - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
    parameters:
      density: 0.53
      weight: 0.35
  - model: ChaoticNeutrals/Eris_7B
    parameters:
      density: 0.53
      weight: 0.25
merge_method: dare_ties
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
  int8_mask: true
dtype: bfloat16