Instructions to use Akerrules/swotmodelQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Akerrules/swotmodelQA with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Akerrules/swotmodelQA",
	filename="model4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Akerrules/swotmodelQA with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akerrules/swotmodelQA
# Run inference directly in the terminal:
llama-cli -hf Akerrules/swotmodelQA

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akerrules/swotmodelQA
# Run inference directly in the terminal:
llama-cli -hf Akerrules/swotmodelQA

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 Akerrules/swotmodelQA
# Run inference directly in the terminal:
./llama-cli -hf Akerrules/swotmodelQA

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 Akerrules/swotmodelQA
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Akerrules/swotmodelQA

Use Docker

docker model run hf.co/Akerrules/swotmodelQA

LM Studio
Jan

vLLM

How to use Akerrules/swotmodelQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akerrules/swotmodelQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Akerrules/swotmodelQA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Akerrules/swotmodelQA

Ollama
How to use Akerrules/swotmodelQA with Ollama:
```
ollama run hf.co/Akerrules/swotmodelQA
```

Unsloth Studio new

How to use Akerrules/swotmodelQA 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 Akerrules/swotmodelQA 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 Akerrules/swotmodelQA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Akerrules/swotmodelQA to start chatting

Pi new

How to use Akerrules/swotmodelQA with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Akerrules/swotmodelQA

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": "Akerrules/swotmodelQA"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Akerrules/swotmodelQA with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Akerrules/swotmodelQA

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 Akerrules/swotmodelQA

Run Hermes

hermes

Docker Model Runner
How to use Akerrules/swotmodelQA with Docker Model Runner:
```
docker model run hf.co/Akerrules/swotmodelQA
```

Lemonade

How to use Akerrules/swotmodelQA with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Akerrules/swotmodelQA

Run and chat with the model

lemonade run user.swotmodelQA-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Llama 3.1 8B Instruct - SWOT Question Generator

Model Description

This model is a fine-tuned version of Llama 3.1 8B Instruct, specifically optimized for generating comprehensive questions for SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. The model has been trained to generate relevant, probing questions that help organizations conduct thorough SWOT analyses across various industries and business contexts.

Intended Use

Business consultants conducting SWOT analyses
Strategic planning teams
Business analysts
Management consultants
Students and educators in business studies
Entrepreneurs developing business plans

Training Details

Base Model

Original Model: Llama 3.1 8B Instruct
Architecture: Transformer-based LLM
Parameters: 8 billion
Original Training: General instruction following and conversation

Fine-tuning

Training Focus: SWOT analysis question generation
Training Data: Curated dataset of SWOT analyses and professional business analysis questions
Training Approach: Instruction fine-tuning with emphasis on business context understanding

Usage Examples

# Example prompt format
prompt = """
Generate relevant questions for conducting a SWOT analysis of [company/industry].
Focus on [specific aspect] if applicable.
"""

# Example usage
prompt = """
Generate relevant questions for conducting a SWOT analysis of a tech startup 
focusing on AI software development.
"""

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Safetensors

Model size

8B params

Tensor type

F16