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RichardErkhov/
instruction-pretrain_-_InstructLM-1.3B-gguf

Name: instruction-pretrain_-_InstructLM-1.3B-gguf
Availability: InStock

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InstructLM-1.3B - GGUF

Model creator: https://huggingface.co/instruction-pretrain/
Original model: https://huggingface.co/instruction-pretrain/InstructLM-1.3B/

Name	Quant method	Size
InstructLM-1.3B.Q2_K.gguf	Q2_K	0.49GB
InstructLM-1.3B.IQ3_XS.gguf	IQ3_XS	0.54GB
InstructLM-1.3B.IQ3_S.gguf	IQ3_S	0.57GB
InstructLM-1.3B.Q3_K_S.gguf	Q3_K_S	0.56GB
InstructLM-1.3B.IQ3_M.gguf	IQ3_M	0.58GB
InstructLM-1.3B.Q3_K.gguf	Q3_K	0.62GB
InstructLM-1.3B.Q3_K_M.gguf	Q3_K_M	0.62GB
InstructLM-1.3B.Q3_K_L.gguf	Q3_K_L	0.67GB
InstructLM-1.3B.IQ4_XS.gguf	IQ4_XS	0.69GB
InstructLM-1.3B.Q4_0.gguf	Q4_0	0.72GB
InstructLM-1.3B.IQ4_NL.gguf	IQ4_NL	0.73GB
InstructLM-1.3B.Q4_K_S.gguf	Q4_K_S	0.73GB
InstructLM-1.3B.Q4_K.gguf	Q4_K	0.77GB
InstructLM-1.3B.Q4_K_M.gguf	Q4_K_M	0.77GB
InstructLM-1.3B.Q4_1.gguf	Q4_1	0.8GB
InstructLM-1.3B.Q5_0.gguf	Q5_0	0.87GB
InstructLM-1.3B.Q5_K_S.gguf	Q5_K_S	0.87GB
InstructLM-1.3B.Q5_K.gguf	Q5_K	0.89GB
InstructLM-1.3B.Q5_K_M.gguf	Q5_K_M	0.89GB
InstructLM-1.3B.Q5_1.gguf	Q5_1	0.95GB
InstructLM-1.3B.Q6_K.gguf	Q6_K	1.03GB
InstructLM-1.3B.Q8_0.gguf	Q8_0	1.33GB

Original model description:

license: apache-2.0 datasets:

tiiuae/falcon-refinedweb
instruction-pretrain/ft-instruction-synthesizer-collection language:
en

Instruction Pre-Training: Language Models are Supervised Multitask Learners

This repo contains the general models pre-trained from scratch in our paper Instruction Pre-Training: Language Models are Supervised Multitask Learners.

We explore supervised multitask pre-training by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of Instruction Pre-Training. Instruction Pre-Training* outperforms Vanilla Pre-training in both general pre-training from scratch and domain-adaptive continual pre-training. In pre-training from scratch, Instruction Pre-Training not only improves pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, Instruction Pre-Training enables Llama3-8B to be comparable to or even outperform Llama3-70B.

Resources

🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗

Context-Based Instruction Synthesizer: instruction-synthesizer
Fine-Tuning Data for the Synthesizer: ft-instruction-synthesizer-collection
General Models Pre-Trained from Scratch:
- InstructLM-500M
- InstructLM-1.3B
Domain-Specific Models Pre-Trained from Llama3-8B:
- Finance-Llama3-8B
- Biomedicine-Llama3-8B
General Instruction-Augmented Corpora: general-instruction-augmented-corpora
Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): medicine-instruction-augmented-corpora

General Pre-Training From Scratch

We augment the RefinedWeb corproa with instruction-response pairs generated by our context-based instruction synthesizer to pre-train general langauge models from scratch.

To evaluate our general base model using the lm-evaluation-harness framework

Setup dependencies:

bash

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .

Evalaute:

bash

MODEL=instruction-pretrain/InstructLM-1.3B
add_bos_token=True # this flag is needed because lm-eval-harness set add_bos_token to False by default, but ours require add_bos_token to be True

accelerate launch -m lm_eval --model hf \
    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16  \
    --gen_kwargs do_sample=False \
    --tasks piqa,hellaswag,winogrande \
    --batch_size auto \
    --num_fewshot 0

accelerate launch -m lm_eval --model hf \
    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16 \
    --gen_kwargs do_sample=False \
    --tasks social_iqa,ai2_arc,openbookqa,boolq,mmlu \
    --batch_size auto \
    --num_fewshot 5

Citation

If you find our work helpful, please cite us:

Instruction Pre-Training

bibtex

@article{cheng2024instruction,
  title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
  author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
  journal={arXiv preprint arXiv:2406.14491},
  year={2024}
}

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