LoneStriker/smol-7b-8.0bpw-h8-exl2

Smol 7B

This model is a fine-tuned version of openchat/openchat_3.5 on the open source dataset HuggingFaceH4/no_robots using the recipes published in The Alignment Handbook.

Model date

rishiraj/smol-7b was trained between 1st and 3rd December, 2023.

Evaluation

It achieves the following results on the Open_LLM_Leaderboard. At the time of release, smol-7b is the highest ranked 7B chat model on the MMLU Benchmark.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
rishiraj/smol-7b	67.11	63.74	84.77	65	46.17	80.66	62.32
argilla/notus-7b-v1	63.49	64.59	84.83	63.04	54.35	79.56	34.57
Intel/neural-chat-7b-v3-1	61.59	66.21	83.64	62.37	59.65	78.14	19.56
HuggingFaceH4/zephyr-7b-beta	61.59	62.46	84.35	60.7	57.83	77.11	27.07
Qwen/Qwen-7B	59.19	51.37	78.47	59.84	47.79	72.69	44.96
microsoft/Orca-2-7b	54.55	54.1	76.19	56.37	52.45	73.48	14.71
01-ai/Yi-6B	54.08	55.55	76.57	64.11	41.96	74.19	12.13

Inference procedure

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="rishiraj/smol-7b", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate"
    },
    {
        "role": "user",
        "content": "How many helicopters can a human eat in one sitting?"
    }
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 128
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
2.0569	0.16	3	2.0409

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.14.6
• Tokenizers 0.14.1

Citation Information

@misc{rishiraj2023smol,
  author = {Rishiraj Acharya},
  title = {Smol 7B},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/rishiraj/smol-7b}}
}