Instructions to use LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base")
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 Settings

vLLM

How to use LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base"
# 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_AI_Mistral_R1_Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base

SGLang

How to use LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base 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_AI_Mistral_R1_Base" \
    --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_AI_Mistral_R1_Base",
		"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_AI_Mistral_R1_Base" \
        --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_AI_Mistral_R1_Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/_Spydaz_Web_AI_Mistral_R1_Base
```

"Success comes from defining each task in achievable steps.

Every completed step is a success that brings you closer to your goal.

Winners create more winners, while losers do the opposite.

Success is a game of winners.

— # Leroy Dyer (1972-Present)

The Human AI .

SpydazWeb AI (7b Mistral) (512k)

This model has been trained to perform with contexts of 512k , although in training it has been trained mainly with the 2048 for general usage : the long context aspect also allows fro advanced projects and sumarys as well as image and audio translationns and generations:

Highly trained as well as methodolgy oriented , this model has been trained on the reAct Prcess and other structured processes . hence structured outputs (json) are very highly trained as well as orchestration of other agents and tasks : the model has been trained for tools use as well as funtion use : as well as custom processes and tools : some tools do not need code either as thier implication means the model may even generate a tool or artifct to perfrom the task :

A New genrea of AI ! This is Trained to give highly detailed humanized responses : Performs tasks well, a Very good model for multipupose use : the model has been trained to become more human in its reposes as well as role playing and story telling : This latest model has been trained on Conversations with a desire to respond with expressive emotive content , As well as discussions on various topics: It has also been focused on conversations by human interactions. hence there maybe NFSW contet in the model : This has no way inhibited its other tasks which were also aligned using the new intensive and Expressive prompt :

Thinking Humanly:

AI aims to model human thought, a goal of cognitive science across fields like psychology and computer science.

Thinking Rationally:

AI also seeks to formalize “laws of thought” through logic, though human thinking is often inconsistent and uncertain.

Acting Humanly:

Turing's test evaluates AI by its ability to mimic human behavior convincingly, encompassing skills like reasoning and language.

Acting Rationally:

Russell and Norvig advocate for AI that acts rationally to achieve the best outcomes, integrating reasoning and adaptability to environments.

BASE MODEL - REASONER

The base model has been created as a new staarting point : It has been fully primed with various types of chains of thoughts and step by step solutions : enabling for reward training to take place . this model has been trained with various languges ( not intensivly ), enabling for cross languge understanding ; Here we create a valid start point for agent based modelling , As we find that some training actually affects existing knowledge , hence agents become a thing ! or if you prefr, distillations .... These agents can be medical , technical , roleplayers etc .

Rewards and modelling reasoning capablitys

Modelling reasoning begins with mathmatics , here we focus where the mdel should have been inesivly pretrained but was not , SO we focus on basic mathmatical tasks , then programming , diagnosis etc : This scheme can be used also with other tasks , such as planning providing structured outputs for the task being performed. as well explanationsif required :

Advance reasoning does not come from chain of thoughts !!! or distilation !!! ... It comes from the ability for the model to create a explanation for exisrting problems , and finding alturnative solutions , then optimising the best solutions whilst learning each route taken to get to the answer : Previously it has been simulating a answer using patern recognition . or recall of a verbatum problem .. SO now we would like it to find the inner part of the task... Ie calculate .. this calccualtion process enables thinking ! We can also use it for emotive responses , and interview techniques . so it ill explain why it asked that particular question or gave that type of response , ie if it was empathic or had sentimental value etc , such as determoining the sentiment of the use and the intent and using this also as a reflective point on the response given and why could it have been different to acheive the same goals !