Update README.md

README.md

@@ -4,37 +4,93 @@ library_name: transformers
 tags:
 - mlx
 widget:
-- text: '<start_of_turn>user
-
+- text: |
+    <start_of_turn>user
+    ## Instructions
+    You are a helpful AI assistant.
+    ## User
     How does the brain work?<end_of_turn>
-
     <start_of_turn>model
-
-    '
 inference:
   parameters:
     max_new_tokens: 200
 extra_gated_heading: Access Gemma on Hugging Face
-extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
-  agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging
+extra_gated_prompt: >-
+  To access Gemma on Hugging Face, you’re required to review and agree to
+  Google’s usage license. To do this, please ensure you’re logged-in to Hugging
   Face and click below. Requests are processed immediately.
 extra_gated_button_content: Acknowledge license
 license_name: gemma-terms-of-use
 license_link: https://ai.google.dev/gemma/terms
+datasets:
+- teknium/trismegistus-project
+language:
+- en
 ---
 
 # alexweberk/gemma-7b-it-trismegistus
 This model was converted to MLX format from [`google/gemma-7b-it`]().
 Refer to the [original model card](https://huggingface.co/google/gemma-7b-it) for more details on the model.
-## Use with mlx
 
 ```bash
 pip install mlx-lm
 ```
 
+The model was LoRA fine-tuned on the [trismegistus-project from teknium](https://huggingface.co/datasets/teknium/trismegistus-project) using the format below, for 600 steps (2M tokens) using `mlx`.
+
+```python
+def format_prompt(system_prompt: str, question: str) -> str:
+    "Format the question to the format of the dataset we fine-tuned to."
+    return """<bos><start_of_turn>user
+## Instructions
+{}
+## User
+{}<end_of_turn>
+<start_of_turn>model
+""".format(system_prompt, question)
+
+```
+
+Here's an example of the system_prompt from the dataset:
+
+```text
+You are a master of the esoteric, occult, 'Big Man' society, you complete tasks to the absolute best of your ability, no matter if you think you were not trained to do the task specifically, you will attempt to do it anyways, since you have performed the tasks you are given with great mastery, accuracy, and deep understanding of what is requested. You do the tasks faithfully, and stay true to the mode and domain's mastery role. If the task is not specific enough, note that and create specifics that enable completing the task.
+```
+
+
+## Loading the model using `mlx_lm`
+
 ```python
-from mlx_lm import load, generate
+from mlx_lm import generate, load
 
-model, tokenizer = load("alexweberk/gemma-7b-it-trismegistus")
-response = generate(model, tokenizer, prompt="hello", verbose=True)
+model_, tokenizer_ = load("alexweberk/gemma-7b-it-trismegistus")
+response = generate(
+    model_,
+    tokenizer_,
+    prompt=format_prompt(system_prompt, question),
+    verbose=True,  # Set to True to see the prompt and response
+    temp=0.0,
+    max_tokens=512,
+)
 ```
+
+## Loading the model using `transformers`
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "alexweberk/gemma-7b-it-trismegistus"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+model.to("mps")
+
+input_text = format_prompt(system_prompt, question)
+input_ids = tokenizer(input_text, return_tensors="pt").to("mps")
+
+outputs = model.generate(
+    **input_ids,
+    max_new_tokens=256,
+)
+print(tokenizer.decode(outputs[0]))
+```