Instructions to use AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M

Use Docker

docker model run hf.co/AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf with Ollama:
```
ollama run hf.co/AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
```

Unsloth Studio

How to use AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-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 AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf with Docker Model Runner:
```
docker model run hf.co/AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M
```

Lemonade

How to use AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AlekseiPravdin/Seamaiiza-7B-v3-32k-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Seamaiiza-7B-v3-32k-gguf-Q4_K_M

List all available models

lemonade list

Seamaiiza-7B-v3-32k

Seamaiiza-7B-v3-32k is a merge of the following models using mergekit:

🧩 Configuration

models:
  - model: grimjim/kunoichi-lemon-royale-v2-32K-7B
    # no parameters necessary for base model
  - model: AlekseiPravdin/Seamaiiza-7B-v1
    parameters:
      weight: 0.2
      density: 0.3
  - model: Nitral-AI/Nyanade_Stunna-Maid-7B
    parameters:
      weight: 0.4
      density: 0.5  
merge_method: dare_ties
base_model: grimjim/kunoichi-lemon-royale-v2-32K-7B
parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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