---
license: mit
datasets:
- Replete-AI/code_bagel
---
# Chatty-McChatterson-3-mini-128k

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6324ce4d5d0cf5c62c6e3c5a/zKJXnm52nly4viTzs0Ysa.png)

## Model Details

**Model Name:** Chatty-McChatterson-3-mini-128k 
**Base Model:** [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)  
**Fine-tuning Method:** Supervised Fine-Tuning (SFT)  
**Dataset:** [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)  
**Training Data:** 12884 conversations selected for being 512 input tokens or less 
**Training Duration:** 4 hours  
**Hardware:** Nvidia RTX A4500  
**Epochs:** 3

## Training Procedure

This model was fine-tuned to provide better instructions on code.

The training was conducted using PEFT and SFTTrainer on select conversations from the Ultra Chat 200k dataset.
Training was completed in 3 epochs (19326 steps) over a span of 4 hours on an Nvidia A4500 GPU.

The dataset comprised of a filterd list of rows from the [Ultra Chat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset, where the prompt template was 512 tokens or less.

## Intended Use

This model is designed to improve the overall chat experience and response quality.

## Getting Started

## Instruct Template
```bash
<|system|>
{system_message} <|end|>
<|user|>
{Prompt) <|end|>
<|assistant|>
```

### Transfromers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name_or_path = "thesven/Chatty-McChatterson-3-mini-128k"

# BitsAndBytesConfig for loading the model in 4-bit precision
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
)

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    device_map="auto",
    trust_remote_code=False,
    revision="main",
    quantization_config=bnb_config
)
model.pad_token = model.config.eos_token_id

prompt_template = '''
<|user|>
What is the name of the big tower in Toronto?.<|end|>
<|assistant|>
'''

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.1, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=256)

generated_text = tokenizer.decode(output[0, len(input_ids[0]):], skip_special_tokens=True)
print(generated_text)
```