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

Libraries

How to use shafire/talktoai with Transformers:

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

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

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

PEFT
How to use shafire/talktoai with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shafire/talktoai with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/shafire/talktoai

SGLang

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

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

talktoaiZERO - Fine-Tuned with AutoTrain

talktoaiZERO is a fine-tuned version of the Meta-Llama-3.1-8B-Instruct model, specifically designed for conversational AI with advanced features in original quantum math quantum thinking and mathematical ethical decision-making. The model was trained using AutoTrain

Features

Base Model: Meta-Llama-3.1-8B-Instruct
Fine-Tuning: Custom conversational training focused on ethical, quantum-based responses.
Use Cases: Ethical mathematical decision-making, advanced conversational AI, and quantum-math-inspired logic in AI responses, intelligent.

Usage

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()

# Sample conversation
messages = [
    {"role": "user", "content": "What are the ethical implications of quantum mechanics in AI systems?"}
]

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: "Quantum mechanics introduces complexity, but the goal remains ethical decision-making."
print(response)

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Model tree for shafire/talktoai

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

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Quantizations

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