Instructions to use ysn-rfd/HelpingAI2.5-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ysn-rfd/HelpingAI2.5-2B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="ysn-rfd/HelpingAI2.5-2B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ysn-rfd/HelpingAI2.5-2B-GGUF", dtype="auto")

llama-cpp-python

How to use ysn-rfd/HelpingAI2.5-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ysn-rfd/HelpingAI2.5-2B-GGUF",
	filename="helpingai2.5-2b-q4_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ysn-rfd/HelpingAI2.5-2B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

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 ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

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 ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

Use Docker

docker model run hf.co/ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use ysn-rfd/HelpingAI2.5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ysn-rfd/HelpingAI2.5-2B-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": "ysn-rfd/HelpingAI2.5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

SGLang

How to use ysn-rfd/HelpingAI2.5-2B-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 "ysn-rfd/HelpingAI2.5-2B-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": "ysn-rfd/HelpingAI2.5-2B-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 "ysn-rfd/HelpingAI2.5-2B-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": "ysn-rfd/HelpingAI2.5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ysn-rfd/HelpingAI2.5-2B-GGUF with Ollama:
```
ollama run hf.co/ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
```

Unsloth Studio

How to use ysn-rfd/HelpingAI2.5-2B-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 ysn-rfd/HelpingAI2.5-2B-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 ysn-rfd/HelpingAI2.5-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ysn-rfd/HelpingAI2.5-2B-GGUF to start chatting

Docker Model Runner
How to use ysn-rfd/HelpingAI2.5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0
```

Lemonade

How to use ysn-rfd/HelpingAI2.5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ysn-rfd/HelpingAI2.5-2B-GGUF:Q4_0

Run and chat with the model

lemonade run user.HelpingAI2.5-2B-GGUF-Q4_0

List all available models

lemonade list

ysn-rfd commited on Mar 28, 2025

Commit

c438394

verified ·

1 Parent(s): 4202e89

Upload README.md with huggingface_hub

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README.md +41 -22

README.md CHANGED Viewed

@@ -14,30 +14,49 @@ tags:
 - Conversational
 - SLM
 - llama-cpp
-- matrixportal
 ---
 # ysn-rfd/HelpingAI2.5-2B-GGUF
-   This model was converted to GGUF format from [`HelpingAI/HelpingAI2.5-2B`](https://huggingface.co/HelpingAI/HelpingAI2.5-2B) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
 Refer to the [original model card](https://huggingface.co/HelpingAI/HelpingAI2.5-2B) for more details on the model.
-## ✅ Quantized Models Download List
-**✨ Recommended for CPU:** `Q4_K_M` | **⚡ Recommended for ARM CPU:** `Q4_0` | **🏆 Best Quality:** `Q8_0`
-| 🚀 Download | 🔢 Type | 📝 Notes |
-|:---------|:-----|:------|
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q2_k.gguf) | ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) | Basic quantization |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q3_k_s.gguf) | ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) | Small size |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q3_k_m.gguf) | ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) | Balanced quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q3_k_l.gguf) | ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) | Better quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q4_0.gguf) | ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) | Fast on ARM |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q4_k_s.gguf) | ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) | Fast, recommended |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q4_k_m.gguf) | ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ | Best balance |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q5_0.gguf) | ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) | Good quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q5_k_s.gguf) | ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) | Balanced |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q5_k_m.gguf) | ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) | High quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q6_k.gguf) | ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 | Very good quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-q8_0.gguf) | ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ | Fast, best quality |
-| [Download](https://huggingface.co/ysn-rfd/HelpingAI2.5-2B-GGUF/resolve/main/helpingai2.5-2b-f16.gguf) | ![F16](https://img.shields.io/badge/F16-000000) | Maximum accuracy |
-💡 **Tip:** Use `F16` for maximum precision when quality is critical

 - Conversational
 - SLM
 - llama-cpp
+- gguf-my-repo
 ---
 # ysn-rfd/HelpingAI2.5-2B-GGUF
+This model was converted to GGUF format from [`HelpingAI/HelpingAI2.5-2B`](https://huggingface.co/HelpingAI/HelpingAI2.5-2B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/HelpingAI/HelpingAI2.5-2B) for more details on the model.
+## Use with llama.cpp
+Install llama.cpp through brew (works on Mac and Linux)
+```bash
+brew install llama.cpp
+```
+Invoke the llama.cpp server or the CLI.
+### CLI:
+```bash
+llama-cli --hf-repo ysn-rfd/HelpingAI2.5-2B-GGUF --hf-file helpingai2.5-2b-q4_0.gguf -p "The meaning to life and the universe is"
+```
+### Server:
+```bash
+llama-server --hf-repo ysn-rfd/HelpingAI2.5-2B-GGUF --hf-file helpingai2.5-2b-q4_0.gguf -c 2048
+```
+Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+Step 1: Clone llama.cpp from GitHub.
+```
+git clone https://github.com/ggerganov/llama.cpp
+```
+Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
+```
+cd llama.cpp && LLAMA_CURL=1 make
+```
+Step 3: Run inference through the main binary.
+```
+./llama-cli --hf-repo ysn-rfd/HelpingAI2.5-2B-GGUF --hf-file helpingai2.5-2b-q4_0.gguf -p "The meaning to life and the universe is"
+```
+or
+```
+./llama-server --hf-repo ysn-rfd/HelpingAI2.5-2B-GGUF --hf-file helpingai2.5-2b-q4_0.gguf -c 2048
+```