library_name: transformers tags:

• code license: llama3 language:
• en pipeline_tag: text-generation

model_id = "Milkyway-islander/Movie_Reivews_Llama-3-8B"

Input Models input text only.

Output Models generate text and code only.

This model is trained and fine tuned on 1500 movie reviews from IMDB movie review dataset. It aims to generate highly human like movie reviews. This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [Amber Zhan]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [Text Generation]
• Language(s) (NLP): [English]
• License: [More Information Needed]
• Finetuned from model [optional]: [Llama3-8b]

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You can run conversational inference by loading model directly

from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config, #quantization is optional attn_implementation= "flash_attention_2", force_download=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right"

inputs = tokenizer(prompt_text, return_tensors="pt", padding=True, truncation=True, max_length=4096).to("cuda") input_ids = inputs['input_ids'] num_input_tokens = input_ids.shape[1] attention_mask = inputs['attention_mask'] # Ensure the attention mask is generated

prompt_text = ""

output = model.generate( **inputs, max_length=4096 + num_input_tokens, # Adjust max_length to account for prompt tokens pad_token_id=tokenizer.eos_token_id )

response = tokenizer.decode(output[0][num_input_tokens:], skip_special_tokens=True)

print(response)

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Use the code below to get started with the model.

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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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