File size: 2,739 Bytes
b9459fe b34fe72 b9459fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# 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:
```python
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
|