HuggingFaceTB
/

cosmo-1b

Text Generation

text-generation-inference

Inference Endpoints

Model card Files Files and versions Community

loubnabnl HF staff commited on Feb 20, 2024

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f1d7a8f

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Update README.md

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README.md +5 -5

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@@ -25,8 +25,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda"  # for GPU usage or "cpu" for CPU usage
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmopedian-1b")
-model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmopedian-1b").to(device)
 prompt = "Generate a story involving a dog, an astronaut and a baker"
 prompt= tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False)
 inputs = tokenizer(prompt, return_tensors="pt").to(device)
@@ -52,8 +52,8 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda"  # for GPU usage or "cpu" for CPU usage
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmopedian-1b")
-model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmopedian-1b").to(device)
 prompt = "Dark matter is"
 inputs = tokenizer(prompt, return_tensors="pt").to(device)
@@ -63,7 +63,7 @@ print(tokenizer.decode(output[0]))
 # Limitations
-Thsi is a small 1.8B model trained on synthetic data, so it might hallucinate, give incomplete or incorrect answers.
 # Training

 device = "cuda"  # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo-1b")
+model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmo-1b").to(device)
 prompt = "Generate a story involving a dog, an astronaut and a baker"
 prompt= tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False)
 inputs = tokenizer(prompt, return_tensors="pt").to(device)
 device = "cuda"  # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo-1b")
+model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmo-1b").to(device)
 prompt = "Dark matter is"
 inputs = tokenizer(prompt, return_tensors="pt").to(device)
 # Limitations
+This is a small 1.8B model trained on synthetic data, so it might hallucinate, give incomplete or incorrect answers.
 # Training