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

Libraries

How to use OEvortex/BabyMistral with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/BabyMistral")
model = AutoModelForCausalLM.from_pretrained("OEvortex/BabyMistral")
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]:]))

llama-cpp-python

How to use OEvortex/BabyMistral with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OEvortex/BabyMistral",
	filename="babymistral-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OEvortex/BabyMistral with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OEvortex/BabyMistral:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OEvortex/BabyMistral:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OEvortex/BabyMistral:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OEvortex/BabyMistral:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OEvortex/BabyMistral:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OEvortex/BabyMistral:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OEvortex/BabyMistral:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OEvortex/BabyMistral:Q4_K_M

Use Docker

docker model run hf.co/OEvortex/BabyMistral:Q4_K_M

LM Studio
Jan

vLLM

How to use OEvortex/BabyMistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OEvortex/BabyMistral:Q4_K_M

SGLang

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

Ollama
How to use OEvortex/BabyMistral with Ollama:
```
ollama run hf.co/OEvortex/BabyMistral:Q4_K_M
```

Unsloth Studio new

How to use OEvortex/BabyMistral 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 OEvortex/BabyMistral 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 OEvortex/BabyMistral to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OEvortex/BabyMistral to start chatting

Docker Model Runner
How to use OEvortex/BabyMistral with Docker Model Runner:
```
docker model run hf.co/OEvortex/BabyMistral:Q4_K_M
```

Lemonade

How to use OEvortex/BabyMistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OEvortex/BabyMistral:Q4_K_M

Run and chat with the model

lemonade run user.BabyMistral-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BabyMistral Model Card

Model Overview

BabyMistral is a compact yet powerful language model designed for efficient text generation tasks. Built on the Mistral architecture, this model offers impressive performance despite its relatively small size.

Key Specifications

Parameters: 1.5 billion
Training Data: 1.5 trillion tokens
Architecture: Based on Mistral
Training Duration: 70 days
Hardware: 4x NVIDIA A100 GPUs

Model Details

Architecture

BabyMistral utilizes the Mistral AI architecture, which is known for its efficiency and performance. The model scales this architecture to 1.5 billion parameters, striking a balance between capability and computational efficiency.

Training

Dataset Size: 1.5 trillion tokens
Training Approach: Trained from scratch
Hardware: 4x NVIDIA A100 GPUs
Duration: 70 days of continuous training

Capabilities

BabyMistral is designed for a wide range of natural language processing tasks, including:

Text completion and generation
Creative writing assistance
Dialogue systems
Question answering
Language understanding tasks

Usage

Getting Started

To use BabyMistral with the Hugging Face Transformers library:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OEvortex/BabyMistral")
tokenizer = AutoTokenizer.from_pretrained("OEvortex/BabyMistral")

# Define the chat input
chat = [
#     { "role": "system", "content": "You are BabyMistral" },
    { "role": "user", "content": "Hey there! How are you? 😊" }
]

inputs = tokenizer.apply_chat_template(
    chat,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)


# Generate text
outputs = model.generate(
    inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    eos_token_id=tokenizer.eos_token_id,

    
)

response = outputs[0][inputs.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

#I am doing well! How can I assist you today? 😊

Ethical Considerations

While BabyMistral is a powerful tool, users should be aware of its limitations and potential biases:

The model may reproduce biases present in its training data
It should not be used as a sole source of factual information
Generated content should be reviewed for accuracy and appropriateness

Limitations

May struggle with very specialized or technical domains
Lacks real-time knowledge beyond its training data
Potential for generating plausible-sounding but incorrect information

Downloads last month: 20

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for OEvortex/BabyMistral

Finetunes

1 model

Quantizations

3 models