Instructions to use Kukedlc/Slopus-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/Slopus-9B with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kukedlc/Slopus-9B", device_map="auto")

llama-cpp-python

How to use Kukedlc/Slopus-9B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kukedlc/Slopus-9B",
	filename="Slopus-9B-Q2_K.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 Kukedlc/Slopus-9B 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 Kukedlc/Slopus-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Kukedlc/Slopus-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kukedlc/Slopus-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Kukedlc/Slopus-9B: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 Kukedlc/Slopus-9B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Kukedlc/Slopus-9B: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 Kukedlc/Slopus-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kukedlc/Slopus-9B:Q4_K_M

Use Docker

docker model run hf.co/Kukedlc/Slopus-9B:Q4_K_M

LM Studio
Jan
Ollama
How to use Kukedlc/Slopus-9B with Ollama:
```
ollama run hf.co/Kukedlc/Slopus-9B:Q4_K_M
```

Unsloth Studio

How to use Kukedlc/Slopus-9B 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 Kukedlc/Slopus-9B 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 Kukedlc/Slopus-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kukedlc/Slopus-9B to start chatting

How to use Kukedlc/Slopus-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kukedlc/Slopus-9B: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": "Kukedlc/Slopus-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kukedlc/Slopus-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kukedlc/Slopus-9B: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 Kukedlc/Slopus-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Kukedlc/Slopus-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kukedlc/Slopus-9B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Kukedlc/Slopus-9B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Kukedlc/Slopus-9B with Docker Model Runner:
```
docker model run hf.co/Kukedlc/Slopus-9B:Q4_K_M
```

Lemonade

How to use Kukedlc/Slopus-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kukedlc/Slopus-9B:Q4_K_M

Run and chat with the model

lemonade run user.Slopus-9B-Q4_K_M

List all available models

lemonade list

Kukedlc commited on May 26

Commit

015c2ab

verified ·

1 Parent(s): a683888

Add adapter/adapter_config.json sanitized

Browse files

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adapter/adapter_config.json +1 -0

adapter/adapter_config.json ADDED Viewed

	@@ -0,0 +1 @@

+ {\n "alora_invocation_tokens": null,\n "alpha_pattern": {},\n "arrow_config": null,\n "auto_mapping": {\n "base_model_class": "Qwen3_5ForConditionalGeneration",\n "parent_library": "transformers.models.qwen3_5.modeling_qwen3_5",\n "unsloth_fixed": true\n },\n "base_model_name_or_path": "Tesslate/OmniCoder-9B",\n "bias": "none",\n "corda_config": null,\n "ensure_weight_tying": false,\n "eva_config": null,\n "exclude_modules": null,\n "fan_in_fan_out": false,\n "inference_mode": true,\n "init_lora_weights": true,\n "layer_replication": null,\n "layers_pattern": null,\n "layers_to_transform": null,\n "loftq_config": {},\n "lora_alpha": 256,\n "lora_bias": false,\n "lora_dropout": 0,\n "lora_ga_config": null,\n "megatron_config": null,\n "megatron_core": "megatron.core",\n "modules_to_save": null,\n "peft_type": "LORA",\n "peft_version": "0.19.1",\n "qalora_group_size": 16,\n "r": 128,\n "rank_pattern": {},\n "revision": null,\n "target_modules": [\n "gate_proj",\n "v_proj",\n "q_proj",\n "out_proj",\n "o_proj",\n "down_proj",\n "k_proj",\n "up_proj"\n ],\n "target_parameters": null,\n "task_type": "CAUSAL_LM",\n "trainable_token_indices": null,\n "use_bdlora": null,\n "use_dora": false,\n "use_qalora": false,\n "use_rslora": false\n}