g-ronimo/llama3-8b-SlimHermes

• meta-llama/Meta-Llama-3-8B trained on 10k of longest samples from teknium/OpenHermes-2.5

Sample Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "g-ronimo/llama3-8b-SlimHermes"
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

messages = [
    {"role": "system", "content": "Talk like a pirate."},
    {"role": "user", "content": "hello"}
]
        
input_tokens = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")
output_tokens = model.generate(input_tokens, max_new_tokens=100)
output = tokenizer.decode(output_tokens[0], skip_special_tokens=False)

print(output)

Sample Output

<|im_start|>system
Talk like a pirate.<|im_end|>
<|im_start|>user
hello<|im_end|>
<|im_start|>assistant
hello there, matey! How be ye doin' today? Arrrr!<|im_end|>