Quantization made by Richard Erkhov.
tiny-random-LlamaForCausalLM-ONNX - GGUF
Original model description:
library_name: transformers
tags: []
Model Card for Model ID
Model Details
Code to generate
1import torch
2from transformers import LlamaForCausalLM, LlamaConfig, AutoTokenizer
3
4# Set seed for reproducibility
5torch.manual_seed(0)
6
7# Initializing the configuration
8configuration = LlamaConfig(
9 head_dim=16,
10 hidden_size=32,
11 intermediate_size=64,
12 max_position_embeddings=131072,
13 model_type="llama",
14 num_attention_heads=2,
15 num_hidden_layers=1,
16 num_key_value_heads=2,
17 rms_norm_eps=1e-05,
18 rope_scaling={
19 "factor": 32.0,
20 "high_freq_factor": 4.0,
21 "low_freq_factor": 1.0,
22 "original_max_position_embeddings": 8192,
23 "rope_type": "llama3"
24 },
25 rope_theta=500000.0,
26 tie_word_embeddings=True,
27 vocab_size=128256,
28)
29
30# Initializing a model from the configuration
31model = LlamaForCausalLM(configuration)
32
33# Re-use tokenizer
34tokenizer = AutoTokenizer.from_pretrained("Xenova/Llama-3.2-Tokenizer")
35
36# Upload to the HF Hub
37model_id = 'onnx-community/tiny-random-LlamaForCausalLM-ONNX'
38model.push_to_hub(model_id)
39tokenizer.push_to_hub(model_id)
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