Text Generation Models
Collection
Here are our state-of-the-art models for completion and chat.
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The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Dromedary - 7B
Dromedary is our uncensored flagship model, designed for programming tasks and communication. Dromedary is a fine-tune of LLAMA-2. Dromedary was fine-tuned on 3 public datasets and on 1 private dataset (synthetic, by our model GPT-LIO.), ranging from programming to healthcare advice.
This model supports both chat and text completion
Technical Information
Dromedary is a model designed to be unbiased and uncensored. However, this model was detoxified. (we dont want any bad people!!!) The model was tuned with two RTX 8000s, at 1 epoch.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("intone/Dromedary-7B")
model = AutoModelForCausalLM.from_pretrained("intone/Dromedary-7B", device_map="auto", torch_dtype='auto')
messages = [
{"role": "user", "content": "Hi, how are you?"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response) # --> "Hello! How can I assist you today?"
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