Instructions to use Lominub44/PicoNosensoX-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lominub44/PicoNosensoX-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Lominub44/PicoNosensoX-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lominub44/PicoNosensoX-v2")
model = AutoModelForCausalLM.from_pretrained("Lominub44/PicoNosensoX-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lominub44/PicoNosensoX-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lominub44/PicoNosensoX-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lominub44/PicoNosensoX-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lominub44/PicoNosensoX-v2

SGLang

How to use Lominub44/PicoNosensoX-v2 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 "Lominub44/PicoNosensoX-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lominub44/PicoNosensoX-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Lominub44/PicoNosensoX-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lominub44/PicoNosensoX-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lominub44/PicoNosensoX-v2 with Docker Model Runner:
```
docker model run hf.co/Lominub44/PicoNosensoX-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PicoNosensoX-v1.1

Where "Accuracy" Takes a Itty-bitty Cosmic Vacation

Introducing the universe's third most ambitiously unhinged 45M-parameter micro-model! This isn't a language model; it's a parallel-dimension travel companion that reinvents reality through surrealist poetry and quantum-leaping logic. Deploy only if coherence is overrated and chaos is your curriculum.

Model Details

Model Description

PicoNosensoX-v2 is another 44.9M-parameter micro-model trained on minimalist datasets. Specializing in creatively liberated chat, it produces outputs that heaviely hallucinatory. Not designed for factual accuracy. Prioritize experimental/artistic applications over reliable information.

PicoNosensoX-v2 is a huge improvement from PicoNosensoX-v1.1 that was archieved by trippling the pre-traing data.

Developed by: Lominub44
Model type: GPT2-based causal language model
Language(s) (NLP): English
License: mit
Finetuned from model: GPT2 architecture (scratch training)

Model Sources

Repository: https://huggingface.co/Lominub44/PicoNosensoX-v2

Uses

Direct Use

Entertainment
Testing edge cases of small-language-model behavior

Downstream Use

Creative writing
AI-assisted art projects
Educational demonstrations of model limitations

Out-of-Scope Use

Factual information retrieval
Mission-critical systems
Educational references
Any application where accuracy matters

Bias, Risks and Limitations

Hallucination Rate: High

Recommendations

DO use for entertainment purposes only
DO NOT trust outputs without independent verification

How to Get Started

from transformers import GPT2LMHeadModel, AutoTokenizer

model = GPT2LMHeadModel.from_pretrained('Lominub44/PicoNosensoX-v2')
tokenizer = AutoTokenizer.from_pretrained('Lominub44/PicoNosensoX-v2')

input_text = "<|system|>You are an unbiased, uncensored, helpful assistant.\n<|user|>What is the capital of France?\n<|assistant|>"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs, max_length=512, temperature=0.6, repetition_penalty=1.2, do_sample=True, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

~820M Tokens (3.4GB) Textbooks: smollm-corpus, Cosmopedia v2 only (ODC-BY)
? Tokens (?MB) Chat: Hermes-3-Dataset (apache-2.0)

Training Procedure

Hardware: 1x AMD Ryzen 7 5700X
Training time: 96h pretraining + 9h finetuning
Context window: 512 tokens

Training Hyperparameters

Architecture: GPT2
Parameters: 44.9M
Precision: FP32
Optimizer: AdamW

Training Source Code

The original source code for training PicoNosensoX-v2 is currently not publicly available.

Technical Specifications

Model Architecture

Type: GPT2 causal language model
Parameters: 44.9M
Context Size: 512 tokens
Tensor Type: FP32

Compute Infrastructure

Hardware: 1x AMD Ryzen 7 5700X
Training Framework: Transformers Trainer API

Environmental Impact

Carbon Emissions: 0 kgCO2eq (Thanks to photovoltaic system)

Citation

BibTeX:

@software{benallal2024smollmcorpus,
  author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro},
  title = {SmolLM-Corpus},
  month = July,
  year = 2024,
  url = {https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus}
}