Upload folder using huggingface_hub

README.md

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+---
+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 with the config from [nvidia/Hymba-1.5B-Instruct](https://huggingface.co/nvidia/Hymba-1.5B-Instruct) but is of smaller size. 
+
+Codes:
+```python
+from huggingface_hub import create_repo, upload_folder
+import os
+
+import torch
+import transformers
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, set_seed
+
+model_id = "nvidia/Hymba-1.5B-Instruct"
+repo_id = "yujiepan/hymba-tiny-random"
+save_path = f"/tmp/{repo_id}"
+
+config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+config.conv_dim = {str(i): 32 for i in range(3)}
+config.hidden_size = 16
+config.intermediate_size = 32
+config.num_attention_heads = 2
+config.num_key_value_heads = 1
+config.v_head_dim = 8
+config.num_hidden_layers = 3
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+tokenizer.save_pretrained(save_path)
+
+model = AutoModelForCausalLM.from_config(
+    config, torch_dtype=torch.bfloat16, 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)
+
+prompt = 'Hello!'
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."}
+]
+messages.append({"role": "user", "content": prompt})
+tokenized_chat = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
+outputs = model.cuda().generate(
+    tokenized_chat,
+    max_new_tokens=16,
+    do_sample=False,
+    temperature=0.7,
+    use_cache=True,
+)
+input_length = tokenized_chat.shape[1]
+response = tokenizer.decode(
+    outputs[0][input_length:], skip_special_tokens=True)
+print(f"Model response: {response}")
+
+os.system(f"ls -alh {save_path}")
+create_repo(repo_id, exist_ok=True)
+upload_folder(repo_id=repo_id, folder_path=save_path)
+```