Instructions to use Biscotto58/CaptainNemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Biscotto58/CaptainNemo 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 Biscotto58/CaptainNemo:Q4_K_M # Run inference directly in the terminal: llama cli -hf Biscotto58/CaptainNemo:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Biscotto58/CaptainNemo:Q4_K_M # Run inference directly in the terminal: llama cli -hf Biscotto58/CaptainNemo:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Biscotto58/CaptainNemo:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Biscotto58/CaptainNemo:Q4_K_M
Use Docker
docker model run hf.co/Biscotto58/CaptainNemo:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Biscotto58/CaptainNemo with Ollama:
ollama run hf.co/Biscotto58/CaptainNemo:Q4_K_M
- Unsloth Desktop
- Pi
How to use Biscotto58/CaptainNemo with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Biscotto58/CaptainNemo:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Biscotto58/CaptainNemo:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Biscotto58/CaptainNemo with Docker Model Runner:
docker model run hf.co/Biscotto58/CaptainNemo:Q4_K_M
- Lemonade
How to use Biscotto58/CaptainNemo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Biscotto58/CaptainNemo:Q4_K_M
Run and chat with the model
lemonade run user.CaptainNemo-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Biscotto58/CaptainNemo with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Biscotto58/CaptainNemo: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 Biscotto58/CaptainNemo:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Biscotto58/CaptainNemo with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Biscotto58/CaptainNemo: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 "Biscotto58/CaptainNemo: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"
|
Download model/README.md from Biscotto58/CaptainNemo: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/Biscotto58/CaptainNemo/resolve/main/model/README.md
- Command line
-
hf download hf://Biscotto58/CaptainNemo/model/README.md
-
curl -L -o README.md https://huggingface.co/Biscotto58/CaptainNemo/resolve/main/model/README.md
1.35 kB
metadata
base_model:
- TheDrummer/UnslopNemo-12B-v3
- inflatebot/MN-12B-Mag-Mell-R1
library_name: transformers
tags:
- mergekit
- merge
final_merge_output
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Task Arithmetic merge method using TheDrummer/UnslopNemo-12B-v3 as a base.
Models Merged
The following models were included in the merge:
- /teamspace/studios/this_studio/work_dir/intermediate_stages/adventure_muse_merge
- /teamspace/studios/this_studio/work_dir/intermediate_stages/unfiltered_creative_merge
- inflatebot/MN-12B-Mag-Mell-R1
Configuration
The following YAML configuration was used to produce this model:
base_model: TheDrummer/UnslopNemo-12B-v3
dtype: bfloat16
merge_method: task_arithmetic
models:
- model: /teamspace/studios/this_studio/work_dir/intermediate_stages/adventure_muse_merge
parameters:
weight: 0.4
- model: /teamspace/studios/this_studio/work_dir/intermediate_stages/unfiltered_creative_merge
parameters:
weight: 0.3
- model: inflatebot/MN-12B-Mag-Mell-R1
parameters:
weight: 0.3
parameters: null