Instructions to use Minibase/Content-Preview-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minibase/Content-Preview-Generator with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Minibase/Content-Preview-Generator",
	filename="model.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Minibase/Content-Preview-Generator with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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 Minibase/Content-Preview-Generator
# Run inference directly in the terminal:
./llama-cli -hf Minibase/Content-Preview-Generator

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 Minibase/Content-Preview-Generator
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Minibase/Content-Preview-Generator

Use Docker

docker model run hf.co/Minibase/Content-Preview-Generator

LM Studio
Jan
Ollama
How to use Minibase/Content-Preview-Generator with Ollama:
```
ollama run hf.co/Minibase/Content-Preview-Generator
```

Unsloth Studio new

How to use Minibase/Content-Preview-Generator 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 Minibase/Content-Preview-Generator 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 Minibase/Content-Preview-Generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Minibase/Content-Preview-Generator to start chatting

Docker Model Runner
How to use Minibase/Content-Preview-Generator with Docker Model Runner:
```
docker model run hf.co/Minibase/Content-Preview-Generator
```

Lemonade

How to use Minibase/Content-Preview-Generator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Minibase/Content-Preview-Generator

Run and chat with the model

lemonade run user.Content-Preview-Generator-{{QUANT_TAG}}

List all available models

lemonade list

Minibase commited on Sep 26, 2025

Commit

0bedb85

verified ·

1 Parent(s): c6ab67a

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +29 -0

config.json ADDED Viewed

	@@ -0,0 +1,29 @@

+{
+  "_name_or_path": "Content-Preview-Generator",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.36.0",
+  "use_cache": true,
+  "vocab_size": 49152,
+  "quantization_config": {
+    "quant_method": "gguf",
+    "quantization_type": "Q8_0"
+  }
+}