---
license: mit
datasets:
- Abirate/english_quotes
- JeanKaddour/minipile
- EleutherAI/wikitext_document_level
- marksverdhei/wordnet-definitions-en-2021
language:
- en
metrics:
- cer
base_model:
- ai21labs/AI21-Jamba-1.5-Mini
---
Trained on 554m tokens, 1 epoch, lr .00987
brown corpus
quotes (wikiquote, azquote, gracious quotes, english quotes)
idioms (scraped)
defitions (wordnet)
wiki_text
mini pile

Trained on runpod for 5 days using 3090

code: https://gist.github.com/thistleknot/368ab298edf596ef50d2cfdcbec66fd1

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Specify the path to the directory where the model is stored
#model_dir = r"C:\Users\User\Documents\wiki\wiki\data science\nlp\research\mamba_brown_trained_556m\mamba_brown_trained\mamba_brown_trained"
model_dir = "/home/user/mamba_brown_trained"

# Load the tokenizer from the local directory
# Load the tokenizer and model (use a causal language model for text generation)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir)
model.to('cuda')

# Now, you can use the model and tokenizer for inference
input_text = "Once upon a time"

# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt").to('cuda')

# Generate output tokens using the model
output_ids = model.generate(**inputs, max_length=50)

# Decode the generated token IDs back into text
decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Print the generated output text
print(decoded_output)
```

Once upon a time, the world is changing.


```
# Now, you can use the model and tokenizer for inference
input_text = "The Fulton County Grand Fair was set for Friday at"
inputs = tokenizer(input_text, return_tensors="pt").to('cuda')

# Generate output tokens using the model with repetition controls
output_ids = model.generate(
    **inputs,
    max_length=256,  # Max tokens to generate
    repetition_penalty=1.2,  # Penalize repeated words
    no_repeat_ngram_size=3,  # Prevent 3-gram repetitions
    temperature=0.9,  # Adjust randomness (lower means more deterministic)
    top_k=50,  # Only sample from top 50 tokens
    top_p=0.9  # Use nucleus sampling to control diversity
)

# Decode the generated token IDs back into text
decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Print the generated output text
print(decoded_output)
```
![image/png](https://cdn-uploads.huggingface.co/production/uploads/62578ad28c6638f8a93e8856/dpDosrj8gUt2puqx5TLt_.png)