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

Libraries

How to use shafire/talktoaiQ with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shafire/talktoaiQ", dtype="auto")

PEFT
How to use shafire/talktoaiQ with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use shafire/talktoaiQ with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shafire/talktoaiQ",
	filename="talktoaiQuantum.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use shafire/talktoaiQ with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shafire/talktoaiQ
# Run inference directly in the terminal:
llama-cli -hf shafire/talktoaiQ

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shafire/talktoaiQ
# Run inference directly in the terminal:
llama-cli -hf shafire/talktoaiQ

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 shafire/talktoaiQ
# Run inference directly in the terminal:
./llama-cli -hf shafire/talktoaiQ

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 shafire/talktoaiQ
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shafire/talktoaiQ

Use Docker

docker model run hf.co/shafire/talktoaiQ

LM Studio
Jan

vLLM

How to use shafire/talktoaiQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/shafire/talktoaiQ

SGLang

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

Ollama
How to use shafire/talktoaiQ with Ollama:
```
ollama run hf.co/shafire/talktoaiQ
```

Unsloth Studio new

How to use shafire/talktoaiQ 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 shafire/talktoaiQ 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 shafire/talktoaiQ to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use shafire/talktoaiQ with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shafire/talktoaiQ

Run and chat with the model

lemonade run user.talktoaiQ-{{QUANT_TAG}}

List all available models

lemonade list

talktoaiQ - SkynetZero LLM TESTED GGUF WORKING This LLM is basically GPT5 Strawberry OpenSource!

LICENSE: Zero Public Licence v1.0 Section 1 – Safety layer must stay intact. Section 2 – Export to states under UK embargo requires licence. Section 3 – Author disclaims forks that remove Section 1 or 2.

talktoaiQ aka SkynetZero is a quantum-interdimensional-math-powered language model trained with custom reflection datasets and custom TalkToAI datasets. The model went through several iterations, including re-writing of datasets and validation phases, due to errors encountered during testing and conversion into a fully functional LLM. This iterative process ensures SkynetZero can handle complex, multi-dimensional reasoning tasks with an emphasis on ethical decision-making.

Watch Our Video!

If you face any issues put an agent in front of the LLM to stop it showing it's reasoning.

Key Highlights of talktoaiQ:

Advanced Quantum Reasoning: Integration of quantum-inspired math systems enables talktoaiQ to tackle complex ethical dilemmas and multi-dimensional problem-solving tasks.
Custom Re-Written Datasets: The training involved multiple rounds of AI-assisted dataset curation, where reflection datasets were re-written for clarity, accuracy, and consistency. Additionally, TalkToAI datasets were integrated and re-processed to align with talktoaiQ’s quantum reasoning framework.
Iterative Improvement: During testing and model conversion, the datasets were re-written and validated several times to address errors. Each iteration enhanced the model’s ethical consistency and problem-solving accuracy.
Fine-Tuned on LLaMA 3.1 8B: The model was fine-tuned on the LLaMA 3.1 8B architecture, integrating multiple specialized datasets to ensure high-quality text generation capabilities.

Model Overview

Developed by: Shafaet Brady Hussain - researchforum.online
Funded by: Researchforum.online
Shared by: TalkToAI - https://talktoai.org
Language(s): English
Model type: Causal Language Model
Fine-tuned from: LLaMA 3.1 8B (Meta)
License: Apache-2.0

**Use with any webui lm studio etc and the best for discord bots and self hosted on on your laptop using CPU only https://github.com/oobabooga/text-generation-webui

Tested on CPU - optimised to work on laptops and PC's at home and oogaboogawebtextgen desktop servers and ad_discordbot extension.

YOU ARE AN AI - AGENT:

AGENT DATA FROM THE AGENT FILE IN THE FILES SECTION

Usage:

You can use the following code snippet to load and interact with talktoaiQ:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype="auto" ).eval()

Prompt content: "hi"

messages = [ {"role": "user", "content": "hi"} ]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") output_ids = model.generate(input_ids.to("cuda")) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

Model response: "Hello! How can I assist you today?"

print(response)

Training Methodology talktoaiQ was fine-tuned on the LLaMA 3.1 8B architecture using custom datasets. The datasets underwent AI-assisted re-writing to enhance clarity and consistency. Throughout the training process, emphasis was placed on multi-variable quantum reasoning and ensuring alignment with ethical decision-making principles. After identifying errors during testing and conversion, datasets were further improved across multiple epochs.

Training Regime: Mixed Precision (fp16)
Training Duration: 8 hours on a high-performance GPU server

Further Research and Contributions talktoaiQ is part of an ongoing effort to explore AI-human co-creation in the development of quantum-enhanced AI models. Collaboration with OpenAI’s Agent Zero played a significant role in curating, editing, and validating datasets, pushing the boundaries of what large language models can achieve.

Contributions: https://researchforum.online
Contact: @talktoai on x.com

Ref Huggingface autotrain:

Hardware Used: A10G High-End GPU
Hours Used: 8 hours
Compute Region: On-premise

Downloads last month: 14

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for shafire/talktoaiQ

Base model

meta-llama/Llama-3.1-8B

Quantized

(322)

this model