Affine-0211-5EeiMfpbdZ5T5MZ6yhnJbYv1WjgjKzJFN4RMMmPxTxcxEtsj-1

13
license:apache-2.0
by
weirek
Language Model
OTHER
New
13 downloads
Early-stage
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Edge AI:
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by Affine-0211-5EeiMfpbdZ5T5MZ6yhnJbYv1WjgjKzJFN4RMMmPxTxcxEtsj-1 with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Affine-whiz-5Di55BGTjiQfcWVKKinaV7gdHXVLRa2akWKohuhw6ifHFSTQpythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "weirek/Affine-whiz-5Di55BGTjiQfcWVKKinaV7gdHXVLRa2akWKohuhw6ifHFSTQ",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("weirek/Affine-whiz-5Di55BGTjiQfcWVKKinaV7gdHXVLRa2akWKohuhw6ifHFSTQ")

# Generate text
messages = [
    {"role": "user", "content": "Hello, how are you?"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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