Update README.md with new model card content

README.md

@@ -8,5 +8,182 @@ tags:
 pipeline_tag: text-generation
 ---
 ### Model Overview
+Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.Weights are release under the [Llama 2 Community License Agreement ](https://ai.meta.com/llama/license/) and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
 
+Model type: An auto-regressive language model based on the transformer architecture.
 
+Fine tuned from model: Llama 2
+
+Uses: 
+The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+
+## Links
+
+* [Vicuna Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/vicuna-quickstart-notebook)
+* [Vicuna API Documentation](coming soon)
+* [Vicuna Model Card](https://huggingface.co/lmsys/vicuna-7b-v1.5#vicuna-model-card)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras
+```
+
+Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
+
+| Preset name           | Parameters | Description   |
+|-----------------------|------------|---------------|
+|` vicuna_1.5_7b_en ` | 6.74B | 7 billion parameter, 32-layer, instruction tuned Vicuna v1.5 model.|
+
+
+Paper: https://arxiv.org/abs/2306.05685
+
+## Example Usage
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Use `generate()` to do text generation.
+```python
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
+vicuna_lm.generate("### HUMAN:\nWhat is Keras? \n### RESPONSE:\n", max_length=500)
+
+# Generate with batched prompts.
+vicuna_lm.generate([
+    "### HUMAN:\nWhat is ML? \n### RESPONSE:\n",
+    "### HUMAN:\nGive me your best brownie recipe.\n### RESPONSE:\n",
+],max_length=500)
+```
+
+Compile the `generate()` function with a custom sampler.
+```python
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
+vicuna_lm.compile(sampler="greedy")
+vicuna_lm.generate("I want to say", max_length=30)
+
+vicuna_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
+vicuna_lm.generate("I want to say", max_length=30)
+```
+
+Use `generate()` without preprocessing.
+```python
+prompt = {
+    # `1` maps to the start token followed by "I want to say".
+    "token_ids": np.array([[1, 306, 864, 304, 1827, 0, 0, 0, 0, 0]] * 2),
+    # Use `"padding_mask"` to indicate values that should not be overridden.
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]] * 2),
+}
+
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
+    "vicuna_1.5_7b_en",
+    preprocessor=None,
+    dtype="bfloat16"
+)
+vicuna_lm.generate(prompt)
+```
+
+Call `fit()` on a single batch.
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
+vicuna_lm.fit(x=features, batch_size=2)
+```
+
+Call `fit()` without preprocessing.
+```python
+x = {
+    "token_ids": np.array([[1, 450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0]] * 2),
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]] * 2),
+}
+y = np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2)
+sw = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)
+
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
+    "vicuna_1.5_7b_en",
+    preprocessor=None,
+    dtype="bfloat16"
+)
+vicuna_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
+```
+
+## Example Usage with Hugging Face URI
+
+```python
+import keras
+import keras_hub
+import numpy as np
+```
+
+Use `generate()` to do text generation.
+```python
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
+vicuna_lm.generate("### HUMAN:\nWhat is Keras? \n### RESPONSE:\n", max_length=500)
+
+# Generate with batched prompts.
+vicuna_lm.generate([
+    "### HUMAN:\nWhat is ML? \n### RESPONSE:\n",
+    "### HUMAN:\nGive me your best brownie recipe.\n### RESPONSE:\n",
+],max_length=500)
+```
+
+Compile the `generate()` function with a custom sampler.
+```python
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
+vicuna_lm.compile(sampler="greedy")
+vicuna_lm.generate("I want to say", max_length=30)
+
+vicuna_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
+vicuna_lm.generate("I want to say", max_length=30)
+```
+
+Use `generate()` without preprocessing.
+```python
+prompt = {
+    # `1` maps to the start token followed by "I want to say".
+    "token_ids": np.array([[1, 306, 864, 304, 1827, 0, 0, 0, 0, 0]] * 2),
+    # Use `"padding_mask"` to indicate values that should not be overridden.
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]] * 2),
+}
+
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
+    "hf://keras/vicuna_1.5_7b_en",
+    preprocessor=None,
+    dtype="bfloat16"
+)
+vicuna_lm.generate(prompt)
+```
+
+Call `fit()` on a single batch.
+```python
+features = ["The quick brown fox jumped.", "I forgot my homework."]
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
+vicuna_lm.fit(x=features, batch_size=2)
+```
+
+Call `fit()` without preprocessing.
+```python
+x = {
+    "token_ids": np.array([[1, 450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0]] * 2),
+    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]] * 2),
+}
+y = np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2)
+sw = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)
+
+vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
+    "hf://keras/vicuna_1.5_7b_en",
+    preprocessor=None,
+    dtype="bfloat16"
+)
+vicuna_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
+```