thesven
/

Chatty-McChatterson-3-mini-128k

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 ---
+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)
+```