Instructions to use anthonym21/RemnantInstruct-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthonym21/RemnantInstruct-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anthonym21/RemnantInstruct-8B-GGUF",
	filename="RemnantInstruct-8B-Q4_K_M.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 anthonym21/RemnantInstruct-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anthonym21/RemnantInstruct-8B-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 anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anthonym21/RemnantInstruct-8B-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 anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf anthonym21/RemnantInstruct-8B-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 anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use anthonym21/RemnantInstruct-8B-GGUF with Ollama:
```
ollama run hf.co/anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use anthonym21/RemnantInstruct-8B-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 anthonym21/RemnantInstruct-8B-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 anthonym21/RemnantInstruct-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anthonym21/RemnantInstruct-8B-GGUF to start chatting

Pi new

How to use anthonym21/RemnantInstruct-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use anthonym21/RemnantInstruct-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use anthonym21/RemnantInstruct-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M
```

Lemonade

How to use anthonym21/RemnantInstruct-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anthonym21/RemnantInstruct-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.RemnantInstruct-8B-GGUF-Q4_K_M

List all available models

lemonade list

RemnantInstruct-8B-GGUF

GGUF quantizations of RemnantInstruct-8B, a SLERP merge combining instruction-following with creative writing capabilities.

Model Details

Base Models:

Qwen/Qwen3-8B - Strong instruction following and reasoning
allura-org/remnant-qwen3-8b - Enhanced creative writing and roleplay

Merge Method: SLERP (Spherical Linear Interpolation)

The merge uses a complementary interpolation strategy:

Self-attention layers: Gradual blend from base to creative (0 -> 0.5 -> 0.3 -> 0.7 -> 1)
MLP layers: Inverse blend (1 -> 0.5 -> 0.7 -> 0.3 -> 0)
Default: 50/50 blend

This approach preserves the base model's instruction-following while incorporating the creative writing capabilities of the remnant fine-tune.

Quantizations

Quant	Size	Description
Q4_K_M	4.7 GB	Balanced quality and size (recommended)
Q5_K_M	5.5 GB	Better quality, slightly larger
Q8_0	8.2 GB	Highest quality quantization

Usage

llama.cpp

./llama-cli -m RemnantInstruct-8B-Q4_K_M.gguf -p "Write a story about..." -n 512

Ollama

ollama run anthonym21/remnantinstruct-8b

LM Studio

Download any GGUF file and load it directly in LM Studio.

Merge Configuration

slices:
  - sources:
      - model: Qwen/Qwen3-8B
        layer_range: [0, 36]
      - model: allura-org/remnant-qwen3-8b
        layer_range: [0, 36]
merge_method: slerp
base_model: Qwen/Qwen3-8B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

License

Apache 2.0 (inherited from Qwen3-8B)

Downloads last month: 16

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

Inference Providers NEW

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

Model tree for anthonym21/RemnantInstruct-8B-GGUF

Qwen/Qwen3-8B

allura-org/remnant-qwen3-8b

Merge model

this model