Instructions to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF to start chatting

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-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": "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-Sentient-3.2-3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-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 QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF: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 "QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF: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"

munish0838 commited on Nov 22, 2024

Commit

7347351

verified ·

1 Parent(s): 829e44d

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README.md +66 -0

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	@@ -0,0 +1,66 @@

+---
+license: creativeml-openrail-m
+datasets:
+- mlabonne/lmsys-arena-human-preference-55k-sharegpt
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Llama
+- Llama-Cpp
+- Llama3.2
+- Instruct
+- 3B
+- bin
+- Sentient
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF
+This is quantized version of [prithivMLmods/Llama-Sentient-3.2-3B-Instruct](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) created using llama.cpp
+# Original Model Card
+## Llama-Sentient-3.2-3B-Instruct Modelfile
+| File Name                               | Size         | Description                             | Upload Status  |
+|-----------------------------------------|--------------|-----------------------------------------|----------------|
+| `.gitattributes`                        | 1.57 kB      | Git attributes configuration file       | Uploaded       |
+| `README.md`                             | 42 Bytes     | Initial commit README                  | Uploaded       |
+| `config.json`                           | 1.04 kB      | Configuration file                      | Uploaded       |
+| `generation_config.json`                | 248 Bytes    | Generation configuration file           | Uploaded       |
+| `pytorch_model-00001-of-00002.bin`      | 4.97 GB      | PyTorch model file (part 1)             | Uploaded (LFS) |
+| `pytorch_model-00002-of-00002.bin`      | 1.46 GB      | PyTorch model file (part 2)             | Uploaded (LFS) |
+| `pytorch_model.bin.index.json`          | 21.2 kB      | Model index file                        | Uploaded       |
+| `special_tokens_map.json`               | 477 Bytes    | Special tokens mapping                  | Uploaded       |
+| `tokenizer.json`                        | 17.2 MB      | Tokenizer JSON file                     | Uploaded (LFS) |
+| `tokenizer_config.json`                 | 57.4 kB      | Tokenizer configuration file            | Uploaded       |
+| Model Type | Size | Context Length | Link |
+|------------|------|----------------|------|
+| GGUF | 3B | - | [🤗 Llama-Sentient-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF) |
+The **Llama-Sentient-3.2-3B-Instruct** model is a fine-tuned version of the **Llama-3.2-3B-Instruct** model, optimized for **text generation** tasks, particularly where instruction-following abilities are critical. This model is trained on the **mlabonne/lmsys-arena-human-preference-55k-sharegpt** dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications.
+### Key Use Cases:
+1. **Conversational AI**: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants.
+2. **Text Generation**: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts.
+3. **Instruction Following**: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance.
+The model uses a **PyTorch-based architecture** and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment.
+### Intended Applications:
+- **Chatbots** for virtual assistance, customer support, or as personal digital assistants.
+- **Content Creation Tools**, aiding in the generation of written materials, blog posts, or automated responses based on user inputs.
+- **Educational and Training Systems**, providing explanations and guided learning experiences in various domains.
+- **Human-AI Interaction** platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks.
+With its strong foundation in instruction-following and conversational contexts, the **Llama-Sentient-3.2-3B-Instruct** model offers versatile applications for both general and specialized domains.