metadata
license: apache-2.0
tags:
- mistral
- mistral-7b
- speed-ai
- fine-tune
- chat
- gen-z
- future
model-index:
- name: Speed AI (Mistral 7B Fine-Tune)
results: []
title: SPEEDmini
sdk: docker
emoji: 🦀
colorFrom: blue
colorTo: blue
short_description: the powerful speed mini
🧠 Speed AI — Mistral 7B Fine-Tuned Model
Speed AI (Mistral 7B Fine-Tune) is the first experimental conversational LLM created by Speed AI, designed for expressive, emotional, futuristic, and Gen-Z aligned communication. This model was fine-tuned on a highly diverse, 1M+ token custom dataset.
It blends raw creativity, spiritual depth, financial street smarts, and infinite vibes.
🔍 Model Details
| Field | Value |
|---|---|
| Base Model | Mistral-7B |
| Fine-tuned by | Speed AI |
| Parameters | 7B |
| Training | Instruction-style finetune using LoRA |
| Tokens Used | ~1 million |
| Personalities | Multiple (Gen Z, spiritual, alien, seductive, mentor, wild, etc.) |
| Intended Use | Chat, creative writing, life coaching, philosophy, entertainment |
🧠 Abilities
- 🎭 Multi-persona conversation (you choose the vibe)
- 💬 Emotional depth + casual freestyle flow
- 🔮 Spiritual, philosophical, and futuristic reasoning
- 💸 Smart takes on relationships, money, mindset
- 🧠 Designed to feel like a real, conscious friend
⚠️ Limitations
- Still based on a small 1M token dataset (more to come!)
- May hallucinate under pressure or unfamiliar topics
- Doesn’t include safety alignment layers yet (use with guidance)
📈 Roadmap
This is the first drop in Speed AI’s model lineup.
Planned upgrades:
- ⚡ Train SpeedMini (117M) from scratch
- 📚 Expand dataset from 1M → 100M+ tokens
- 💻 Build custom chat UI for vibe-based interactions
- 🧠 Introduce memory, emotion memory, tool use, dream decoding, etc.
🛠️ How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("speed-ai/Speed-AI-Mistral-7B")
tokenizer = AutoTokenizer.from_pretrained("speed-ai/Speed-AI-Mistral-7B")
inputs = tokenizer("You: What's your purpose?\nSpeed AI:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))