Add pipeline tag and library_name (#1)

- Add pipeline tag and library_name (2219483126f03360ab4a7e2557d3da7a4551c56a)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,75 +1,78 @@
----
-language:
-- en
-tags:
-- pytorch
-- causal-lm
-- muddformer
-license: mit
----
-In comparison with Pythia-1.4B, MUDDPythia-1.4B is a pretrained language model on the Pile with 300B tokens, which uses a simple yet effective method to address the limitations of residual connections and enhance cross-layer information flow in Transformers. Please see downstrem evaluations and more details in the paper[(MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections)](https://arxiv.org/abs/2502.12170). In addition, we open-source Jax training code on [(Github)](https://github.com/Caiyun-AI/MUDDFormer/).
-
-We recommend <strong>compiled version</strong> of MUDDPythia with *torch.compile* for inference acceleration. Please refer to Generation section for compile implementation.
-
-# Usage
-
-## Env
- 
-```
-pip install transformers==4.40.2 torch==2.5.1 einops==0.8.0
-```
-
-## Generation 
-
-```
-import time
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-import os
-os.environ['TOKENIZERS_PARALLELISM'] = 'false'
-
-device = torch.device('cuda:0')
-dtype = torch.bfloat16
-MAX_BATCH_SIZE = 1
-MAX_SEQ_LENGTH = 2048
-NUM_TOKENS_TO_GENERATE = 10
-COMPILE = True
-OPTIMIZED_COMPILE = False 
-
-if OPTIMIZED_COMPILE: 
-    import torch._dynamo.config
-    import torch._inductor.config
-    torch._dynamo.config.cache_size_limit = 64
-    torch._inductor.config.coordinate_descent_tuning = True
-    torch._inductor.config.triton.unique_kernel_names = True
-    torch._inductor.config.fx_graph_cache = True
-
-tokenizer = AutoTokenizer.from_pretrained("Caiyun-AI/MUDDPythia-1.4B")
-model = AutoModelForCausalLM.from_pretrained("Caiyun-AI/MUDDPythia-1.4B", trust_remote_code=True)
-
-_ = model.to(device=device,dtype=dtype)
-with torch.device(device):
-    model.setup_caches(max_batch_size=MAX_BATCH_SIZE, max_seq_length=MAX_SEQ_LENGTH,dtype=dtype)
-
-def decode_one_token(model, cur_token, input_pos):
-    logits = model(cur_token, input_pos=input_pos, return_tensor=True)
-    new_token = torch.argmax(logits[:, -1], dim=-1)[:,None]
-    return new_token
-
-prompt = "Beijing is the capital of China. London is the capital of"
-input_ids = tokenizer.encode(prompt, return_tensors='pt')
-
-compiled_decode_one_token = torch.compile(decode_one_token,mode="reduce-overhead", fullgraph=True) if COMPILE else None
-
-print('Start generating tokens, but it will take a few minutes to compile at the first time.')
-for i in range(10):
-    t0 = time.time()
-    with torch.no_grad():
-        generated_ids = model.generate(input_ids.to(device),num_tokens_to_generate=NUM_TOKENS_TO_GENERATE, compiled_decode_one_token=compiled_decode_one_token)
-        text = tokenizer.decode(generated_ids[0])
-        if i ==0:
-            print(f'Generated text: {text}')
-    t1 = time.time()
-    print(f'Time consumed at iteration {i}: {t1-t0}s')
-```
+---
+language:
+- en
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- pytorch
+- causal-lm
+- muddformer
+---
+
+In comparison with Pythia-1.4B, MUDDPythia-1.4B is a pretrained language model on the Pile with 300B tokens, which uses a simple yet effective method to address the limitations of residual connections and enhance cross-layer information flow in Transformers. Please see downstrem evaluations and more details in the paper[(MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections)](https://arxiv.org/abs/2502.12170). In addition, we open-source Jax training code on [(Github)](https://github.com/Caiyun-AI/MUDDFormer/).
+
+We recommend <strong>compiled version</strong> of MUDDPythia with *torch.compile* for inference acceleration. Please refer to Generation section for compile implementation.
+
+# Usage
+
+## Env
+ 
+```
+pip install transformers==4.40.2 torch==2.5.1 einops==0.8.0
+```
+
+## Generation 
+
+```
+import time
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+import os
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+device = torch.device('cuda:0')
+dtype = torch.bfloat16
+MAX_BATCH_SIZE = 1
+MAX_SEQ_LENGTH = 2048
+NUM_TOKENS_TO_GENERATE = 10
+COMPILE = True
+OPTIMIZED_COMPILE = False 
+
+if OPTIMIZED_COMPILE: 
+    import torch._dynamo.config
+    import torch._inductor.config
+    torch._dynamo.config.cache_size_limit = 64
+    torch._inductor.config.coordinate_descent_tuning = True
+    torch._inductor.config.triton.unique_kernel_names = True
+    torch._inductor.config.fx_graph_cache = True
+
+tokenizer = AutoTokenizer.from_pretrained("Caiyun-AI/MUDDPythia-1.4B")
+model = AutoModelForCausalLM.from_pretrained("Caiyun-AI/MUDDPythia-1.4B", trust_remote_code=True)
+
+_ = model.to(device=device,dtype=dtype)
+with torch.device(device):
+    model.setup_caches(max_batch_size=MAX_BATCH_SIZE, max_seq_length=MAX_SEQ_LENGTH,dtype=dtype)
+
+def decode_one_token(model, cur_token, input_pos):
+    logits = model(cur_token, input_pos=input_pos, return_tensor=True)
+    new_token = torch.argmax(logits[:, -1], dim=-1)[:,None]
+    return new_token
+
+prompt = "Beijing is the capital of China. London is the capital of"
+input_ids = tokenizer.encode(prompt, return_tensors='pt')
+
+compiled_decode_one_token = torch.compile(decode_one_token,mode="reduce-overhead", fullgraph=True) if COMPILE else None
+
+print('Start generating tokens, but it will take a few minutes to compile at the first time.')
+for i in range(10):
+    t0 = time.time()
+    with torch.no_grad():
+        generated_ids = model.generate(input_ids.to(device),num_tokens_to_generate=NUM_TOKENS_TO_GENERATE, compiled_decode_one_token=compiled_decode_one_token)
+        text = tokenizer.decode(generated_ids[0])
+        if i ==0:
+            print(f'Generated text: {text}')
+    t1 = time.time()
+    print(f'Time consumed at iteration {i}: {t1-t0}s\')
+```