Upload folder using huggingface_hub

README.md

@@ -8,7 +8,7 @@ widget:
 library_name: transformers
 ---
 
-This model is randomly initialized, using the config from [keyfan/grok-1-hf](https://huggingface.co/keyfan/grok-1-hf) but with smaller size. 
+This model is randomly initialized, using the config from [hpcai-tech/grok-1](https://huggingface.co/hpcai-tech/grok-1/blob/main/config.json) but with smaller size. 
 Note the model is in float16.
 
 Codes:
@@ -18,36 +18,42 @@ import torch
 import os
 from huggingface_hub import create_repo, upload_folder
 
-source_model_id = 'keyfan/grok-1-hf'
-save_path = '/tmp/yujiepan/grok-1-tiny-random'
-repo_id = 'yujiepan/grok-1-tiny-random'
+source_model_id = 'hpcai-tech/grok-1'
+tiny_random_name = 'grok-1-tiny-random'
+save_path = f'/tmp/yujiepan/{tiny_random_name}'
+repo_id = f'yujiepan/{tiny_random_name}'
 
 config = transformers.AutoConfig.from_pretrained(
     source_model_id, trust_remote_code=True)
 config.hidden_size = 4
 config.intermediate_size = 8
-config.num_attention_heads = 4
-config.num_experts_per_tok = 2
+config.num_attention_heads = 2
+config.num_key_value_heads = 1
 config.num_hidden_layers = 2
-config.num_key_value_heads = 2
-config.num_local_experts = 8
 config.torch_dtype = torch.float16
 
 model = transformers.AutoModelForCausalLM.from_config(
-    config, trust_remote_code=True)
+    config, trust_remote_code=True, torch_dtype=torch.float16)
 model = model.half()
-model.save_pretrained(save_path)
 
 tokenizer = transformers.AutoTokenizer.from_pretrained(
     source_model_id, trust_remote_code=True)
-tokenizer.save_pretrained(save_path)
 
 result = transformers.pipelines.pipeline(
     'text-generation',
-    model=model.float(), tokenizer=tokenizer)('Hello')
+    model=model, tokenizer=tokenizer,
+    device=0,
+    max_new_tokens=16,
+)('Hello')
 print(result)
+# model = model.cuda()
+# response, history = model.chat(tokenizer, "Hi", history=[], max_length=32)
+# print(response)
+
+model.save_pretrained(save_path)
+tokenizer.save_pretrained(save_path)
 
 os.system(f'ls -alh {save_path}')
 create_repo(repo_id, exist_ok=True)
 upload_folder(repo_id=repo_id, folder_path=save_path)
-```
+```