suresh2001/llama-3.2-1b-instruct-finetuned

Fine-tuned Llama 3.2 1B Instruct

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct.

Model Details

• Base Model: meta-llama/Llama-3.2-1B-Instruct
• Model Type: Causal Language Model
• Architecture: Llama 3.2
• Parameters: ~1.2B
• Fine-tuning: Custom fine-tuned model

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("suresh2001/llama-3.2-1b-instruct-finetuned")
model = AutoModelForCausalLM.from_pretrained(
    "suresh2001/llama-3.2-1b-instruct-finetuned",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Generate text
prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=100,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Architecture

This model follows the Llama 3.2 architecture with:

• 16 transformer layers
• 32 attention heads
• 2048 hidden size
• 8192 intermediate size
• 131072 max position embeddings
• RoPE (Rotary Position Embedding) with Llama 3 scaling

Training Details

This model was fine-tuned from the base Llama 3.2 1B Instruct model. The specific training details and dataset information would depend on your fine-tuning process.

Intended Use

This model is designed for instruction-following tasks and conversational AI applications. It can be used for:

• Text generation
• Question answering
• Creative writing
• Code generation
• General conversation

Limitations

• This model inherits the limitations of the base Llama 3.2 1B model
• Performance may vary depending on the specific fine-tuning data and objectives
• As with all language models, outputs should be carefully reviewed for accuracy and appropriateness

Ethical Considerations

Please use this model responsibly and in accordance with Meta's Llama 3.2 license and usage policies.