Instructions to use OnlyCheeini/greesychat-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OnlyCheeini/greesychat-turbo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OnlyCheeini/greesychat-turbo")
model = AutoModelForCausalLM.from_pretrained("OnlyCheeini/greesychat-turbo")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OnlyCheeini/greesychat-turbo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OnlyCheeini/greesychat-turbo

SGLang

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

Unsloth Studio new

How to use OnlyCheeini/greesychat-turbo 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 OnlyCheeini/greesychat-turbo 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 OnlyCheeini/greesychat-turbo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OnlyCheeini/greesychat-turbo to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="OnlyCheeini/greesychat-turbo",
    max_seq_length=2048,
)

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

GreesyChat-Turbo AI Model

Overview

GreesyChat-Turbo is an advanced AI model designed for robust text generation using the LLaMA 3 architecture. This model excels in providing high-quality responses for general conversation, mathematical queries, and more. It’s perfect for powering chatbots, virtual assistants, and any application requiring intelligent dialogue capabilities.

Benchmark Results

Metric	Value
Perplexity	22.5
Generation Speed	75 ms per token
Accuracy	70%
Response Time	200 ms

Metric	GreesyChat-Turbo	Mixtral-8x7b	GPT-4
Code	79.2	75.6	83.6
MMLU	74.5	79.9	85.1
Gms8k	89.2 (5)	88.7	94.2

Contact

For support or inquiries, please contact: mail@nicatdcw.dev

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for OnlyCheeini/greesychat-turbo

Base model

unsloth/llama-3-8b-Instruct-bnb-4bit

Finetuned

(1142)

this model

Quantizations

3 models

OnlyCheeini
/

greesychat-turbo

GreesyChat-Turbo AI Model

Overview

Benchmark Results

Contact

Model tree for OnlyCheeini/greesychat-turbo

Dataset used to train OnlyCheeini/greesychat-turbo