### Load Toolformer Model and Tokenizer for Inference

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

Loads the Toolformer model and tokenizer from Hugging Face for text generation. Ensure you have the transformers library installed. This snippet is for setting up the inference pipeline.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained(r"dmayhem93/toolformer_v0_epoch2")
model = AutoModelForCausalLM.from_pretrained(
    r"dmayhem93/toolformer_v0_epoch2",
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
).cuda()
generator = pipeline(
    "text-generation", model=model, tokenizer=tokenizer, device=0
)
```

--------------------------------

### Set up and Use Retriever for Data Retrieval

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

Initializes a Retriever tool and demonstrates its usage for retrieving relevant strings from a dataset based on a query. This snippet requires a 'retrieval_test_data.json' file.

```python
from tools import Retriever
import json


if __name__ == '__main__':
    retriever = Retriever()
    ret_val = "location of New Orleans"
    with open('retrieval_test_data.json', encoding='utf-8') as f:
        ret_strings = json.load(f)
    print(', '.join(retriever.retrieval(
        ret_strings, ret_val, 3
    )))
```

--------------------------------

### Run Data Generator with Multiple Devices

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

Launches the data generation script to run on multiple devices concurrently. Adjust 'x' to the total number of devices and 'y' to the specific device ID for this instance.

```bash
python data_generator.py --num_devices=x, --device_id=y
```

--------------------------------

### Toolformer Training Command

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

This command is used to fine-tune the GPT-J 6B model for Toolformer. It specifies batch size, epochs, output directory, dataset, tokenizer, and various training parameters including deepspeed configuration.

```bash
deepspeed train_gptj_toolformer.py --model_name_or_path=EleutherAI/gpt-j-6B --per_device_train_batch_size=4 \
  --num_train_epochs 10 --save_strategy=epoch --output_dir=finetune_toolformer_v0 --report_to "wandb" \
  --dataset_name dmayhem93/toolformer-v0-postprocessed --tokenizer_name customToolformer \
  --block_size 2048 --gradient_accumulation_steps 1 --do_train --do_eval --evaluation_strategy=epoch \
  --logging_strategy=epoch --fp16 --overwrite_output_dir --adam_beta1=0.9 --adam_beta2=0.999 \
  --weight_decay=2e-02 --learning_rate=1e-05 --warmup_steps=100 --per_device_eval_batch_size=1 \
  --cache_dir="hf_cache" --gradient_checkpointing=True --deepspeed ds_config_gpt_j.json
```

--------------------------------

### Run Data Generator on a Single Device

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

Executes the data generation script on a single device, typically used when only one GPU is available. Sets the number of devices to 1 and the device ID to 0.

```bash
python data_generator.py --num_devices=1, --device_id=0
```

--------------------------------

### FlashAttention Paper Citation

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

BibTeX entry for the paper 'Flashattention: Fast and memory-efficient exact attention with io-awareness'.

```bibtex
@Article{dao2022flashattention,
    title={Flashattention: Fast and memory-efficient exact attention with io-awareness},
    author={Dao, Tri and Fu, Daniel Y and Ermon, Stefano and Rudra, Atri and R{'e}, Christopher},
    journal={arXiv preprint arXiv:2205.14135},
    year={2022}
}
```

--------------------------------

### Toolformer Paper Citation

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

BibTeX entry for the Toolformer paper, 'Toolformer: Language Models Can Teach Themselves to Use Tools'.

```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.04761,
  doi = {10.48550/ARXIV.2302.04761},
  
  url = {https://arxiv.org/abs/2302.04761},
  
  author = {Schick, Timo and Dwivedi-Yu, Jane and Dessì, Roberto and Raileanu, Roberta and Lomeli, Maria and Zettlemoyer, Luke and Cancedda, Nicola and Scialom, Thomas},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Toolformer: Language Models Can Teach Themselves to Use Tools},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```

--------------------------------

### Set CUDA Visible Devices for Data Generation

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

Configures the CUDA_VISIBLE_DEVICES environment variable to specify which GPU(s) the data generation script should use. This is useful in multi-GPU environments.

```bash
export CUDA_VISIBLE_DEVICES=5
python data_generator.py --num_devices=8, --device_id=5
```

--------------------------------

### Long Context Transformer Citation

Source: https://github.com/conceptofmind/toolformer/blob/main/README.md

BibTeX entry for the software 'Long Context Transformer v0.0.1'.

```bibtex
@software{Liang_Long_Context_Transformer_2023,
    author = {Liang, Kaizhao},
    doi = {10.5281/zenodo.7651809},
    month = {2},
    title = {{Long Context Transformer v0.0.1}},
    url = {https://github.com/github/linguist},
    version = {0.0.1},
    year = {2023}
}
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.