Instructions to use Mike0021/pulpie-orange-small-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mike0021/pulpie-orange-small-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mike0021/pulpie-orange-small-gguf",
	filename="pulpie-orange-small-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mike0021/pulpie-orange-small-gguf:Q4_K_M

Use Docker

docker model run hf.co/Mike0021/pulpie-orange-small-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use Mike0021/pulpie-orange-small-gguf with Ollama:
```
ollama run hf.co/Mike0021/pulpie-orange-small-gguf:Q4_K_M
```

Unsloth Studio

How to use Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-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 Mike0021/pulpie-orange-small-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mike0021/pulpie-orange-small-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Mike0021/pulpie-orange-small-gguf with Docker Model Runner:
```
docker model run hf.co/Mike0021/pulpie-orange-small-gguf:Q4_K_M
```

Lemonade

How to use Mike0021/pulpie-orange-small-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mike0021/pulpie-orange-small-gguf:Q4_K_M

Run and chat with the model

lemonade run user.pulpie-orange-small-gguf-Q4_K_M

List all available models

lemonade list

pulpie-orange-small-gguf / README.md

Mike0021

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metadata

license: cc-by-nc-4.0
base_model: feyninc/pulpie-orange-small
pipeline_tag: token-classification
tags:
  - gguf
  - eurobert
  - token-classification
  - html
  - content-extraction
  - pulpie

Pulpie Orange Small — GGUF

GGUF conversion of feyninc/pulpie-orange-small, a 210M parameter EuroBERT token-classification model for Pulpie HTML main-content extraction.

⚠️ Not a language model

This is an encoder classifier, not a causal LM. The GGUF files expose per-token other/main classifier logits via llama-embedding, not llama-cli generation.

Files

Quant	Size	Notes
F16	431 MB	Full precision baseline
Q8_0	233 MB	8-bit, verified accurate
Q6_K	182 MB
Q5_K_M	169 MB
Q4_K_M	157 MB
Q3_K_M	143 MB
Q2_K	130 MB	Most aggressive quantization

Verification

Variant	Max diff vs PyTorch	E2E extraction	Prediction agreement
F16	0.070	✅ 3 main blocks	100%
Q8_0	0.072	✅ 3 main blocks	100%
Q6_K – Q2_K	not tested	not tested	—

Note: Only F16 and Q8_0 were numerically verified against the original PyTorch model. Lower quants (Q6_K → Q2_K) passed load + inference checks but output consistency was not validated. Use F16 or Q8_0 for production.

Full results: verification_report.json

Usage

llama-embedding \
  --model pulpie-orange-small-Q8_0.gguf \
  --prompt "<html><body><p>Hello world</p></body></html><|sep|>" \
  --pooling none \
  --embd-normalize -1 \
  --embd-output-format json

Each output row is [other_logit, main_logit]. Pulpie classifies at <|sep|> token positions and keeps blocks where main_logit > other_logit.

llama-cli can load the files but cannot generate — these GGUFs have no causal LM head.

Python example

import json, re, subprocess

out = subprocess.check_output([
    "llama-embedding", "-m", "pulpie-orange-small-Q8_0.gguf",
    "-p", html_chunk,
    "--pooling", "none",
    "--embd-normalize", "-1",
    "--embd-output-format", "json",
], text=True)

data = json.loads(re.search(r'\{.*\}', out, re.S).group(0))
logits = [row["embedding"] for row in data["data"]]

Conversion notes

Stock llama.cpp supports EuroBERT embeddings but not EuroBertForTokenClassification. A 103-line patch was applied to:

Register EuroBertForTokenClassification in the HF converter
Map classifier.weight/classifier.bias → GGUF cls.output.*
Write classifier labels ["other", "main"] with embedding_length_out=2
Apply the classifier head in the EuroBERT runtime graph

Quantization was done with llama-quantize from the patched build.