yujiepan_-_llama-3.1-tiny-random-4bits

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llama-3.1-tiny-random - bnb 4bits

• Model creator: https://huggingface.co/yujiepan/
• Original model: https://huggingface.co/yujiepan/llama-3.1-tiny-random/

Original model description:

library_name: transformers pipeline_tag: text-generation inference: true widget:

• text: Hello! example_title: Hello world group: Python

---

This model is for debugging. It is randomly initialized using the config from meta-llama/Meta-Llama-3.1-70B-Instruct but with smaller size.

"yujiepan/llama-3.1-tiny-random" and "yujiepan/meta-llama-3.1-tiny-random" share exactly the same files except the repo name.

Codes:

import os

import torch
import transformers
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, set_seed

model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct"
repo_id = "yujiepan/meta-llama-3.1-tiny-random"
save_path = f"/tmp/{repo_id}"

config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config._name_or_path = model_id
config.hidden_size = 8
config.intermediate_size = 16
config.num_attention_heads = 2
config.num_key_value_heads = 1
config.num_hidden_layers = 2
config.torch_dtype = "bfloat16"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)

model = AutoModelForCausalLM.from_config(
    config, torch_dtype=torch.bfloat16, attn_implementation="sdpa", trust_remote_code=True
)
model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True)

set_seed(42)
with torch.no_grad():
    for _, p in sorted(model.named_parameters()):
        torch.nn.init.uniform_(p, -0.2, 0.2)

model.save_pretrained(save_path)

pipe = pipeline("text-generation", model=save_path, device="cuda", trust_remote_code=True, max_new_tokens=20)
print(pipe("Hello World!"))