yujiepan_-_mistral-v0.3-tiny-random-gguf

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mistral-v0.3-tiny-random - GGUF

• Model creator: https://huggingface.co/yujiepan/
• Original model: https://huggingface.co/yujiepan/mistral-v0.3-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 mistralai/Mistral-7B-Instruct-v0.3 but with smaller size.

Codes:

from transformers import pipeline
from huggingface_hub import create_repo, upload_folder
import torch
import transformers
import os

model_id = 'mistralai/Mistral-7B-Instruct-v0.3'
save_path = '/tmp/yujiepan/mistral-v0.3-tiny-random'
repo_id = 'yujiepan/mistral-v0.3-tiny-random'

config = transformers.AutoConfig.from_pretrained(model_id)
config.hidden_size = 8
config.intermediate_size = 32
config.num_attention_heads = 4
config.num_hidden_layers = 2
config.num_key_value_heads = 2
config.head_dim = 2
print(config)

tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(save_path)

model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16)
model.generation_config = transformers.GenerationConfig.from_pretrained(model_id)

transformers.set_seed(42)
with torch.no_grad():
    for _, p in sorted(model.named_parameters()):
        torch.nn.init.uniform_(p, -0.1, 0.1)

pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda')
print(pipe('Hello World!'))

model.save_pretrained(save_path)