Should you’re seeking to fine-tune giant language fashions (LLMs) utilizing Ollama and wish to leverage the scalability of Vertex AI on Google Cloud Platform, you’re not alone. The draw is apparent: Ollama’s developer-friendly interface paired with GCP’s managed infrastructure looks like a match made in machine studying heaven.
However when you dive into implementation, the cracks begin to present. Effective-tuning isn’t all the time easy crusing — particularly once you’re pairing a local-first device like Ollama with a cloud-native platform like Vertex AI.
This put up breaks down the actual limitations you’ll face when fine-tuning Ollama fashions on Vertex AI and what you are able to do about it.
Ollama provides restricted assist for fine-tuning fashions like llama2, mistral, and codellama. It follows a minimalistic CLI-based method:
ollama run llama2
ollama create mymodel --modelfile ./Modelfile
You possibly can move coaching knowledge by way of prompt-style formatting (as textual content), however Ollama isn’t designed for large-scale fine-tuning throughout distributed infrastructure. It’s light-weight by design.
Vertex AI, however, helps mannequin coaching and tuning by way of customized containers, AutoML, or fine-tuning pre-trained fashions within the Mannequin Backyard. However it expects structured datasets, TFRecord/CSV/JSONL codecs, and particular mannequin structure hooks.
1. Lack of Multi-Node Coaching Help
Ollama doesn’t assist distributed coaching out of the field. This creates a bottleneck on Vertex AI, the place TPU/GPU clusters are designed for scalable coaching jobs.
Let’s say you attempt to construct a customized container for Vertex AI that wraps Ollama’s CLI:
FROM ollama/ollama:newest
COPY prepare.txt /app/prepare.txt
RUN ollama create mymodel --modelfile /app/Modelfile
You’ll run into two issues:
- The ollama runtime isn’t optimized for GCP {hardware} accelerators
- There’s no option to shard the coaching set throughout a number of nodes
Vertex AI’s CustomJob useful resource expects you to deal with coaching loops explicitly (typically utilizing frameworks like PyTorch or TensorFlow). With Ollama, you lose management of the internals.
2. Knowledge Ingestion Doesn’t Scale
Ollama expects fine-tuning knowledge in flat textual content prompt-response format. This turns into inefficient when working with datasets saved in Cloud Storage or BigQuery.
Instance of anticipated format
### Instruction:
Write a perform to reverse a string.### Response:
def reverse_string(s):
return s[::-1]
With bigger datasets (10k+ entries), loading these into Ollama in-memory doesn’t scale.
Distinction this with Vertex AI’s anticipated codecs like:
{
"inputs": "Write a perform to reverse a string.",
"outputs": "def reverse_string(s):n return s[::-1]"
}
You’ll want to write down an information transformation layer to transform structured knowledge into Ollama’s immediate format — one thing not natively supported within the CLI workflow.
3. No Native Help for Vertex AI Mannequin Registry
Effective-tuning a mannequin with Vertex AI usually ends in a clear handoff:
- Register the mannequin within the Mannequin Registry
- Deploy it to an endpoint
- Monitor it utilizing Vertex Mannequin Monitoring
With Ollama? Not a lot. Effective-tuned fashions are saved domestically or exported as .bin recordsdata. You’ll have to construct your personal bridge:
ollama export mymodel > mannequin.bin
Then:
- Retailer mannequin.bin in Cloud Storage
- Use a customized prediction routine to load it
- Deploy by way of customized container on Vertex AI
Loads of plumbing — simply to do what Vertex AI usually handles mechanically.
Should you’re useless set on utilizing Ollama for fine-tuning in a cloud atmosphere, contemplate the next hybrid method:
✅ Use Ollama for Light-weight Pre-Tuning
Run light-weight, few-shot fine-tuning periods on native/dev environments with Ollama. Take a look at your dataset, confirm your immediate formatting, and validate the mannequin habits earlier than transferring to manufacturing.
✅ Convert Skilled Fashions to HuggingFace-Appropriate Format
If doable, export the mannequin in a format that may be loaded by transformers and deployed on Vertex AI:
ollama export mymodel > mannequin.bin
Then use this with a customized serving container that wraps HuggingFace mannequin loaders.
Use a Docker picture to encapsulate:
- Knowledge loading
- Immediate formatting
- Ollama execution
- Mannequin exporting
Instance Dockerfile:
FROM ubuntu:20.04
RUN apt replace && apt set up -y curl unzip
RUN curl -fsSL https://ollama.com/set up.sh | shCOPY prepare.txt /app/prepare.txt
COPY Modelfile /app/Modelfile
WORKDIR /app
RUN ollama create mymodel --modelfile Modelfile
CMD ["ollama", "run", "mymodel"]
Deploy utilizing Vertex AI’s CustomJob with a single employee pool:
from google.cloud import aiplatformaiplatform.CustomJob(
display_name="ollama-fine-tune",
worker_pool_specs=[{
"machine_spec": {"machine_type": "n1-standard-4"},
"replica_count": 1,
"container_spec": {"image_uri": "gcr.io/my-project/ollama-fine-tune"},
}]
).run()
Ollama is good for developer-side experiments, however it’s not production-tuning prepared. Vertex AI is constructed for that — however expects full transparency into mannequin internals.
Attempting to fine-tune Ollama fashions on Vertex AI immediately is like becoming a sq. peg in a spherical gap.
You possibly can bridge the 2 with customized wrappers, conversion scripts, and containers — however don’t count on native integration or full observability.
Use Ollama for early-stage fine-tuning and mannequin exploration. When it’s time to scale or go multi-user, both:
- Convert your mannequin to HuggingFace format, or
- Swap to Vertex AI’s native tuning move utilizing Mannequin Backyard or AutoML.
