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README.md

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+
+---
+
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/BabyMistral-GGUF
+This is quantized version of [OEvortex/BabyMistral](https://huggingface.co/OEvortex/BabyMistral) created using llama.cpp
+
+# Original Model Card
+
+
+# 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
+
+