Instructions to use formulae/mita-v1.0-7b-2-24-2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use formulae/mita-v1.0-7b-2-24-2025 with Transformers:

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

pipe = pipeline("text-generation", model="formulae/mita-v1.0-7b-2-24-2025")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("formulae/mita-v1.0-7b-2-24-2025")
model = AutoModelForCausalLM.from_pretrained("formulae/mita-v1.0-7b-2-24-2025")
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 formulae/mita-v1.0-7b-2-24-2025 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "formulae/mita-v1.0-7b-2-24-2025"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "formulae/mita-v1.0-7b-2-24-2025",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/formulae/mita-v1.0-7b-2-24-2025

SGLang

How to use formulae/mita-v1.0-7b-2-24-2025 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 "formulae/mita-v1.0-7b-2-24-2025" \
    --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": "formulae/mita-v1.0-7b-2-24-2025",
		"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 "formulae/mita-v1.0-7b-2-24-2025" \
        --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": "formulae/mita-v1.0-7b-2-24-2025",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use formulae/mita-v1.0-7b-2-24-2025 with Docker Model Runner:
```
docker model run hf.co/formulae/mita-v1.0-7b-2-24-2025
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Formulae/MITA-V1.0-7B-2-24-2025

Overview

Formulae/MITA-V1.0-7B is the first-generation MITA model, designed as a general-purpose, uncensored model. This version serves as the foundation for future MoE (Mixture of Experts) developments.

Built using the TIES merging method, MITA-V1.0-7B combines multiple fine-tuned models to create a balanced and robust generalist model.

Merge Details

Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Merged Models:
- HumanLLMs/Human-Like-Qwen2.5-7B-Instruct
- jeffmeloy/Qwen2.5-7B-nerd-uncensored-v0.9
Merge Method: TIES (Trim, Elect Sign & Merge)
Data Type: bfloat16

What is TIES?

TIES (Trim, Elect Sign & Merge) is a model merging technique designed to preserve valuable parameters when combining multiple models. Unlike naive merging methods, TIES minimizes parameter interference by:

Resetting parameters that changed minimally during fine-tuning.
Resolving sign conflicts between different models.
Merging only the aligned parameters to ensure stability.

📖 Reference: TIES Paper

Capabilities

✅ General-Purpose Intelligence – Balanced across multiple tasks.
✅ Uncensored Outputs – Designed for open and unrestricted conversation.
✅ Strong Reasoning – Maintains logical coherence across different domains.

Limitations & Risks

⚠ No Task Specialization – While versatile, it does not excel in any specific domain like coding or math.
⚠ Potential Biases – As an uncensored model, users should verify outputs for accuracy and ethical considerations.