Instructions to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW with Transformers:

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

pipe = pipeline("text-generation", model="Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW

SGLang

How to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW 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 "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW" \
    --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": "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW",
		"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 "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW" \
        --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": "Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW 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 Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW 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 Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW",
    max_seq_length=2048,
)

Docker Model Runner
How to use Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW with Docker Model Runner:
```
docker model run hf.co/Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW
```

LoreWeaver-2-LoRA_PREVIEW

Modello LLM creato da EleutherAI/pythia-2.8b, specializzato per il RolePlay a turni brevi o di media lunghezza. I migliori casi d'uso per questo modello sono(oltre al RolePlay): Recitazione, scrittura di personaggi basandosi su loro interazioni con contesti(anche ben strutturati e complessi), sperimentazione dinamiche di evoluzione di un personaggio immaginario in nel mondo reale.

Attenzione: Questo modello è solo una anteprima di LoreWeaver-2 e potrebbe non essere completamente coerente, allineato o efficente. Fai inferenza con cautela. Non basarti su questo modello per creare finetunes o ricerche, per via della sua instabilità.

Altre versioni della famiglia LoreWeaver-2:

Tutti i modelli della famiglia LoreWeaver-2, si trovano in questa Raccolta

Architettura

Backbone: EleutherAI/pythia-2.8b
Hidden size: ~3200
Layers: 26–32
Heads: 32
Rotary Embeddings (RoPE)
RMSNorm
SwiGLU
KV-cache

Tokenizer (BPE GPT-Neo)

Normalizzazione Unicode (NFKC)
Vocab size (prevista): 50k–65k
Lingue supportate: Inglese, Italiano, Francese, Tedesco, Spagnolo

Datasets utilizzati

Dataset di linguaggio	Tedesco	Inglese	Francese	Italiano	Spagnolo
HuggingFaceFW/fineweb-edu		☑️
BramVanroy/CommonCrawl-CreativeCommons-fine	☑️	☑️	☑️	☑️	☑️
OpenLLM-France/wikipedia	☑️		☑️	☑️	☑️

Datasets di RolePlay

Tecnologie di ottimizzazione dell'addestramento

Ottimizzazioni di memoria (VRAM)

Gradient Checkpointing
Mixed Precision: bf16/fp16 (rilevato automaticamente durante l'addestramento)
Gradient Accumulation
Ottimizzazione del'uso diretto della VRAM con Unsloth.
- Unsloth FastLanguageModel, UnslothTrainer

Ottimizzazioni di attenzione

FlashAttention 2
TF32 (solo su Ampere+)
AdamW fused (se disponibile)
Weight decay selettivo

Ottimizzazioni del learning rate

Cosine LR Scheduler
Warmup lungo

Ottimizzazioni sul dataset

Curriculum Learning (soft)
Turn-aware training

Ottimizzazioni Semantiche

Tone tokens
Safe-stop token

Deep learning (Contextual Learning)

Contextual Learning permette al modello di non cambiare i pesi e ricodificare il contesto a ogni turno.

Downloads last month: -; Downloads are not tracked for this model. How to track

Datasets used to train Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW

Collection including Croc-Prog-HF/LoreWeaver-2-LoRA_PREVIEW

LoreWeaver-2 Family

Collection

LoreWeaver-2 is a family of Text Generation models, based on the Gpt-Neo architecture. Trained for make creative and imaginative text. • 5 items • Updated Mar 21

Evaluation results

Intended Use
self-reported

Single-turn role-play dialogue generation with contextual awareness.
Context Handling
self-reported

Attention-based contextual learning using structured prompts and KV cache.
Response Length
self-reported

Optimized for short, single-utterance replies per turn.
Memory Mechanism
self-reported

Text-based memory handled through prompt context (no weight updates at inference).