test with ollama

.gitignore

@@ -1,4 +1,5 @@
 src/ollama/ollama
+venv/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -6,4 +6,5 @@ Preferred only MIT sources and open-weights
 
 - v0
     - ollama + open-webui + mistral-7B + docker
-    - vscodium + continue + ollama + mistral-7B
+    - vscodium + continue + ollama + mistral-7B
+    - python + langchain

docs/tune-nodels.md

@@ -0,0 +1,15 @@
+Fine tune models with Private Dataset
+
+- Mistral Format - JSONL
+- {"text":"<s>[INST] Instruction[/INST] Model answer</s>[INST] Follow-up instruction[/INST]"}
+
+
+- Install
+    - pip install langchain
+
+Reference
+- https://github.com/SkunkworksAI/BakLLaVA
+- https://python.langchain.com/docs/integrations/llms/ollama
+- https://www.datacamp.com/tutorial/fine-tuning-llama-2
+- https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/
+- https://jsonlines.org/

src/ollama/bhoomi/llm_image.py

@@ -0,0 +1,45 @@
+from langchain_community.llms import Ollama
+
+bakllava = Ollama(model="bakllava")
+
+
+import base64
+from io import BytesIO
+
+from IPython.display import HTML, display
+from PIL import Image
+
+
+def convert_to_base64(pil_image):
+    """
+    Convert PIL images to Base64 encoded strings
+
+    :param pil_image: PIL image
+    :return: Re-sized Base64 string
+    """
+
+    buffered = BytesIO()
+    pil_image.save(buffered, format="JPEG")  # You can change the format if needed
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    return img_str
+
+
+def plt_img_base64(img_base64):
+    """
+    Display base64 encoded string as image
+
+    :param img_base64:  Base64 string
+    """
+    # Create an HTML img tag with the base64 string as the source
+    image_html = f'<img src="data:image/jpeg;base64,{img_base64}" />'
+    # Display the image by rendering the HTML
+    display(HTML(image_html))
+
+
+file_path = "../../../static/img/ollama_example_img.jpg"
+pil_image = Image.open(file_path)
+image_b64 = convert_to_base64(pil_image)
+plt_img_base64(image_b64)
+
+llm_with_image_context = bakllava.bind(images=[image_b64])
+llm_with_image_context.invoke("What is the dollar based gross retention rate:")

src/ollama/bhoomi/main.py

@@ -1,3 +1,15 @@
 print("Welcome to Bhoomi")
 
+from langchain_community.llms import Ollama
 
+llm = Ollama(model="mistral")
+
+# test 1
+
+# llm.invoke("Tell me a joke")
+
+# test 2
+query = "Tell me a joke"
+
+for chunks in llm.stream(query):
+    print(chunks)