Instructions to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF",
	filename="pagestorm-research-preview-14b-full-book-Q4_K_M.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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "markldn/pagestorm-research-preview-14b-full-book-Q8_0-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": "markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M

Ollama
How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with Ollama:
```
ollama run hf.co/markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
```

Unsloth Studio

How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-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 markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M
```

Lemonade

How to use markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull markldn/pagestorm-research-preview-14b-full-book-Q8_0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.pagestorm-research-preview-14b-full-book-Q8_0-GGUF-Q4_K_M

List all available models

lemonade list

pagestorm-research-preview-14b-full-book-Q8_0-GGUF / README.md

markldn

Add Q4_K_M GGUF

7e7495f verified 5 days ago

preview code

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	---
	license: apache-2.0
	language:
	- en
	base_model: Pageshift-Entertainment/pagestorm-research-preview-14b-full-book
	base_model_relation: quantized
	tags:
	- gguf
	- llama.cpp
	- story-generation
	- staged-generation
	- full-book
	- ministral3
	pipeline_tag: text-generation
	---

	# PageStorm Research Preview 14B Full Book — GGUF

	GGUF quantizations of
	[Pageshift-Entertainment/pagestorm-research-preview-14b-full-book](https://huggingface.co/Pageshift-Entertainment/pagestorm-research-preview-14b-full-book),
	a `ministral3` model trained to produce a full novel from a single prompt via a
	staged generation pipeline.

	## Files
	- `pagestorm-research-preview-14b-full-book-Q8_0.gguf` (~14 GB)
	- `pagestorm-research-preview-14b-full-book-Q4_K_M.gguf` (~7.7 GB)

	## Requirements
	- A llama.cpp build whose runtime supports the `mistral3` architecture
	(`llm_build_mistral3` / `LLM_ARCH_MISTRAL3`). Older builds will fail to load it.

	## Notes
	- The Q8_0 file was converted with `convert_hf_to_gguf.py --outtype q8_0`.
	- The Q4_K_M file was quantized from a temporary BF16 GGUF exported from the
	original BF16 Hugging Face checkpoint, not requantized from Q8_0.
	- The source `config.json` needed `original_max_position_embeddings` changed
	from `16384.0` to integer `16384` so the converter could write the int rope
	KV field.
	- The model uses a staged protocol with custom role headers
	(`<\|start_header_id\|>…<\|stop_header_id\|>`) and `<\|eot_id\|>` as the stage stop
	token — it is not a plain chat model. See the base model card and its
	`story_stage_generation.py` for the prompt protocol.
	- Native context is 262144; KV at that length is large — quantize the KV cache
	(`--cache-type-k q8_0 --cache-type-v q8_0`) and/or cap `--ctx-size` to fit VRAM.

	## Attribution
	Base model © Pageshift Entertainment, Apache-2.0. This repo only redistributes a
	quantized copy of those weights.