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Update README.md with new model card content
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README.md
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@@ -54,7 +54,7 @@ import numpy as np
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Use `generate()` to do text generation.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en"
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llama_lm.generate("What is Keras?", max_length=500)
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# Generate with batched prompts.
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Compile the `generate()` function with a custom sampler.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en"
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llama_lm.compile(sampler="greedy")
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llama_lm.generate("I want to say", max_length=30)
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Call `fit()` on a single batch.
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```python
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features = ["The quick brown fox jumped.", "I forgot my homework."]
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en"
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llama_lm.fit(x=features, batch_size=2)
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```
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Use `generate()` to do text generation.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en"
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llama_lm.generate("What is Keras?", max_length=500)
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# Generate with batched prompts.
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Compile the `generate()` function with a custom sampler.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en"
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llama_lm.compile(sampler="greedy")
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llama_lm.generate("I want to say", max_length=30)
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@@ -159,7 +159,7 @@ llama_lm.generate(prompt)
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Call `fit()` on a single batch.
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```python
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features = ["The quick brown fox jumped.", "I forgot my homework."]
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en"
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llama_lm.fit(x=features, batch_size=2)
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```
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Use `generate()` to do text generation.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en")
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llama_lm.generate("What is Keras?", max_length=500)
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# Generate with batched prompts.
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Compile the `generate()` function with a custom sampler.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en")
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llama_lm.compile(sampler="greedy")
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llama_lm.generate("I want to say", max_length=30)
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Call `fit()` on a single batch.
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```python
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features = ["The quick brown fox jumped.", "I forgot my homework."]
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en")
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llama_lm.fit(x=features, batch_size=2)
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```
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Use `generate()` to do text generation.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
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llama_lm.generate("What is Keras?", max_length=500)
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# Generate with batched prompts.
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Compile the `generate()` function with a custom sampler.
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```python
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
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llama_lm.compile(sampler="greedy")
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llama_lm.generate("I want to say", max_length=30)
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Call `fit()` on a single batch.
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```python
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features = ["The quick brown fox jumped.", "I forgot my homework."]
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llama_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
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llama_lm.fit(x=features, batch_size=2)
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```
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