Instructions to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive", set_active=True)

Transformers

How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive

SGLang

How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive 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 "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive 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 LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive 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 LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive",
    max_seq_length=2048,
)

Docker Model Runner
How to use LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/_Spydaz_Web_LCARS_AdvancedHuman_Archive
```

LeroyDyer commited on Jul 19, 2025

Commit

9fca966

verified ·

1 Parent(s): 543bb26

Update README.md

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README.md +99 -11

README.md CHANGED Viewed

@@ -3,21 +3,109 @@ base_model:
 - LeroyDyer/_Spydaz_Web_LCARS_AdvancedHumanAI
 - LeroyDyer/_Spydaz_Web_AGI_DeepThinker_LCARS_
 - LeroyDyer/_Spydaz_Web_ONTOLOGY_OFFICER_
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- mistral
-- Spydaz
-- SpydazWeb
-- AGI
-- LCARS
 license: apache-2.0
 language:
-- en
 datasets:
-- LeroyDyer/Humanization_001
-- LeroyDyer/QA_Organized_Reasoning_dataset_001
 pipeline_tag: text-generation
 library_name: adapter-transformers
 ---

 - LeroyDyer/_Spydaz_Web_LCARS_AdvancedHumanAI
 - LeroyDyer/_Spydaz_Web_AGI_DeepThinker_LCARS_
 - LeroyDyer/_Spydaz_Web_ONTOLOGY_OFFICER_
+metrics:
+  - accuracy
+  - bertscore
+  - bleu
+  - brier_score
+  - cer
+  - character
+  - charcut_mt
+  - chrf
+  - code_eval
 tags:
+  - text-generation-inference
+  - transformers
+  - unsloth
+  - mistral
+  - trl
+  - chemistry
+  - biology
+  - legal
+  - art
+  - music
+  - finance
+  - code
+  - medical
+  - merge
+  - climate
+  - chain-of-thought
+  - tree-of-knowledge
+  - forest-of-thoughts
+  - visual-spacial-sketchpad
+  - alpha-mind
+  - knowledge-graph
+  - entity-detection
+  - encyclopedia
+  - wikipedia
+  - stack-exchange
+  - Reddit
+  - Cyber-series
+  - MegaMind
+  - Cybertron
+  - SpydazWeb
+  - Spydaz
+  - LCARS
+  - star-trek
+  - mega-transformers
+  - Mulit-Mega-Merge
+  - Multi-Lingual
+  - Afro-Centric
+  - African-Model
+  - Ancient-One
 license: apache-2.0
 language:
+  - en
+  - sw
+  - ig
+  - so
+  - es
+  - ca
 datasets:
+  - gretelai/synthetic_text_to_sql
+  - HuggingFaceTB/cosmopedia
+  - teknium/OpenHermes-2.5
+  - Open-Orca/SlimOrca
+  - Open-Orca/OpenOrca
+  - cognitivecomputations/dolphin-coder
+  - databricks/databricks-dolly-15k
+  - yahma/alpaca-cleaned
+  - uonlp/CulturaX
+  - mwitiderrick/SwahiliPlatypus
+  - swahili
+  - Rogendo/English-Swahili-Sentence-Pairs
+  - ise-uiuc/Magicoder-Evol-Instruct-110K
+  - meta-math/MetaMathQA
+  - abacusai/ARC_DPO_FewShot
+  - abacusai/MetaMath_DPO_FewShot
+  - abacusai/HellaSwag_DPO_FewShot
+  - HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
+  - HuggingFaceFW/fineweb
+  - occiglot/occiglot-fineweb-v0.5
+  - omi-health/medical-dialogue-to-soap-summary
+  - keivalya/MedQuad-MedicalQnADataset
+  - ruslanmv/ai-medical-dataset
+  - Shekswess/medical_llama3_instruct_dataset_short
+  - ShenRuililin/MedicalQnA
+  - virattt/financial-qa-10K
+  - PatronusAI/financebench
+  - takala/financial_phrasebank
+  - Replete-AI/code_bagel
+  - athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
+  - IlyaGusev/gpt_roleplay_realm
+  - rickRossie/bluemoon_roleplay_chat_data_300k_messages
+  - jtatman/hypnosis_dataset
+  - Hypersniper/philosophy_dialogue
+  - Locutusque/function-calling-chatml
+  - bible-nlp/biblenlp-corpus
+  - DatadudeDev/Bible
+  - Helsinki-NLP/bible_para
+  - HausaNLP/AfriSenti-Twitter
+  - aixsatoshi/Chat-with-cosmopedia
+  - HuggingFaceTB/cosmopedia-100k
+  - HuggingFaceFW/fineweb-edu
+  - m-a-p/CodeFeedback-Filtered-Instruction
+  - heliosbrahma/mental_health_chatbot_dataset
 pipeline_tag: text-generation
 library_name: adapter-transformers
 ---