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amgadhasan/
sera-llama-3.1-8b-0.1

Name: sera-llama-3.1-8b-0.1
Availability: InStock

pytorch

llama

llama-3

text-generation-inference

text-generation

conversational

meta-llama/Llama-3.1-8B-Instruct

finetune

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Sera Llama v0.1

This a finetune of Llama3.1-8B on custom tool call to be used as an agent for a personal assistant, network admin. It generates structured outputs with a tool call based on the user's input, without the need to add lengthy system message.

How to use

Use with transformers

python

# pip install -U accelerate bitsandbytes

import transformers

tokenizer = transformers.AutoTokenizer.from_pretrained("Sera-Network/sera-llama-3.1-8b-0.1")

# 4-bit quantization to run on smaller gpus
model = transformers.AutoModelForCausalLM.from_pretrained("Sera-Network/sera-llama-3.1-8b-0.1", device_map="auto", quantization_config=transformers.BitsAndBytesConfig(load_in_4bit=True))

# Warm up the model
input_ids = tokenizer.apply_chat_template([{"role": "user", "content": "What's the capital of France?"}], add_generation_prompt=True, return_tensors='pt').to('cuda')
output = model.generate(input_ids, do_sample=False, max_new_tokens=128)
preds = output[:, input_ids.shape[1]:]
text = tokenizer.decode(preds[0], skip_special_tokens=True)

# Create a helper function to generate text based on user input
def generate(user_input: str):
    input_ids = tokenizer.apply_chat_template([{"role": "user", "content": user_input}], add_generation_prompt=True, return_tensors='pt').to('cuda')
    output = model.generate(input_ids, do_sample=False, max_new_tokens=128)
    preds = output[:, input_ids.shape[1]:]
    text = tokenizer.decode(preds[0], skip_special_tokens=True)
    return text

Example output