Instructions to use Flaxyditto/FlaxyDitto-Bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flaxyditto/FlaxyDitto-Bot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Flaxyditto/FlaxyDitto-Bot")
model = AutoModelForCausalLM.from_pretrained("Flaxyditto/FlaxyDitto-Bot")
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 Flaxyditto/FlaxyDitto-Bot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Flaxyditto/FlaxyDitto-Bot

SGLang

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

Unsloth Studio

How to use Flaxyditto/FlaxyDitto-Bot 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 Flaxyditto/FlaxyDitto-Bot 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 Flaxyditto/FlaxyDitto-Bot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Flaxyditto/FlaxyDitto-Bot to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Flaxyditto/FlaxyDitto-Bot",
    max_seq_length=2048,
)

Docker Model Runner
How to use Flaxyditto/FlaxyDitto-Bot with Docker Model Runner:
```
docker model run hf.co/Flaxyditto/FlaxyDitto-Bot
```

FlaxyDitto-Bot / README.md

Flaxyditto

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	---
	base_model: FlaxyDitto/FlaxyDitto-Bot
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- GGUF
	- Ollama
	- LMStudio
	- transformers
	- unsloth
	- llama_cpp_python
	- ctransformers
	- unsloth
	- vLLM
	---

	# Model Card for Model ID

	This model can code apps quickly and smartly, reaching beautiful GUIs without errors.


	## Model Details



	### Model Description

	This model is capable of coding apps in multiple coding languages with libraries and everything needed. It can code almost anything, can be used for chatting too.
	FlaxyDitto Bot can even be used in ollama, model has been published as "FlaxyDitto-Bot" on Ollama. You can run it almost everywere.



	- Developed by: [FlaxyDitto]
	- Model type: Large Language Model(LLM)
	- Language(s) (NLP): [English]
	- License: [Apache 2.0]

	### Model Sources [optional]



	- Repository: [FlaxyDitto/FlaxyDitto-Bot]

	## Uses

	Coding, Talking, Agentic.

	### Direct Use

	Currently only through python scripts and apps like Ollama, Unsloth, LMStudio...


	## How to Get Started with the Model

	Use Ollama to run the model.

	## Current issues

	Model thinks he is Deepseek. It doesn't happen on the ollama version(it is the official quantinzation from the quantinzations).