OpenHermes-2.5-Mistral-7B-top-SPIN-iter0

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This model is a fine-tuned version of teknium/OpenHermes-2.5-Mistral-7B on the argilla/10k_prompts_top_SPIN_iter0 dataset.

WIP:

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EXAMPLE:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0")
tokenizer = AutoTokenizer.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0")

inputs = tokenizer.apply_chat_template(
    message,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to("cuda"),
    max_new_tokens=1024,
    temperature=0.5,
    do_sample=True
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))

Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Training Details

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Training Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-07
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2.0

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.15.2

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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