When you can code, the procedure is simple.
“(Since the models used in Ollama are pre-converted to GGUF files) Find the corresponding Hugging Face Transformers format repository (one repository or folder per model as a rule) or convert and generate it from GGUF → Fine-tune the Hugging Face Transformers format model using QLoRA → Merge QLoRA into the base model → Convert it into a single GGUF file using the Python script included with Llama.cpp.”
This completes the procedure.
However, there are several algorithmic or computational constraints.
- LLMs, especially smaller ones used by civilians, are structurally unsuited for accurately storing data (from a human perspective). Therefore, RAG—a hybrid of a vector database, an LLM, and several other components—is vastly more computationally efficient for accurate data retrieval.
- For the reason above, when using an LLM as the core of RAG, what’s typically required of the LLM is the ability to process retrieved information accurately. Popular models are often perfectly usable as-is. Plus, if you apply fine-tuning to give it domain-specific prior knowledge, low-cost fine-tuning should easily lead to overall performance improvements.
Therefore, if accuracy of knowledge is the priority, I personally recommend a RAG-like approach. Mainly for cost reasons… There are also many existing open-source frameworks, so it shouldn’t be too much trouble. Fine-tuning is optional.
What a “single LLM file like Ollama uses” actually is
A single file that Ollama runs is not a bundle of documents. It’s a model checkpoint (weights + tokenizer metadata) packaged for inference. In the llama.cpp ecosystem, that file is typically GGUF (.gguf). GGUF is explicitly designed for fast loading and inference with GGML-based executors. (GitHub)
So, if you want your own information “inside one file,” the mechanism is training (fine-tuning) so the model’s weights change to reflect patterns/answers from your material. It will not behave like a perfect document database.
Can this be done?
Yes—with important expectations:
-
Yes, you can create a single .gguf and import it into Ollama using a Modelfile (FROM /path/to/model.gguf). (Ollama Documentation)
-
Yes, you can fine-tune a base model using LoRA/QLoRA adapters, then either:
- ship an adapter (smaller download), or
- merge it into the base and ship a single merged model. Ollama documents importing GGUF adapters via
ADAPTER /path/to/file.gguf and warns the adapter must match the base it was created from. (Ollama Documentation)
-
Reality check: fine-tuning is best for behavior (style, format, common Q&A patterns, policy-like responses). It is weaker for faithful recall across many documents than retrieval systems.
What file format would it need to be?
For sharing and running in Ollama
.gguf is the standard “single file” inference format for Ollama/llama.cpp-style runtimes. (GitHub)- Ollama packaging uses a Modelfile as the “recipe” (FROM, PARAMETER, TEMPLATE, SYSTEM, ADAPTER, etc.). (Ollama Documentation)
For training (your working format)
Training is usually done in a Hugging Face/PyTorch layout (a folder with weights + tokenizer/config). After training, you convert to GGUF for inference. Hugging Face’s own docs note llama.cpp can convert Transformers models to GGUF via convert_hf_to_gguf.py. (Hugging Face)
What you need to know (background, clearly)
1) Three ways people try to “put their data into a model”
You’re aiming for #2/#3.
-
Prompting only (no training): You paste content into the prompt each time.
- No single-file “knowledge,” limited by context size.
-
Instruction fine-tuning (most practical): You convert your material into many examples (question → answer, excerpt → answer, summarize → bullets) and do supervised fine-tuning (SFT).
- This is the standard entry point because it’s controllable and works with limited compute. TRL’s SFTTrainer is a common way to do this. (Hugging Face)
-
Continued pretraining: You train on raw text for longer.
- Harder to control; more compute; easier to accidentally bake in unwanted text.
Key idea: Models learn best from tasks (“when asked X, respond Y”), not from dumping raw documents.
2) Why LoRA/QLoRA matters for your 12GB GPU
Full fine-tuning updates all weights and is often too heavy on consumer hardware. LoRA trains a small set of “adapter” parameters instead.
- Hugging Face PEFT documents that you can merge adapters into the base using
merge_and_unload() so the result behaves like a standalone model. (Hugging Face)
This is the practical path to: “train cheaply → optionally merge → export to one file.”
3) Windows reality: how to avoid environment pain
Many training stacks are smoother on Linux. If you hit dependency friction on Windows, the standard workaround is WSL2 (Linux on Windows) with GPU acceleration:
- NVIDIA’s CUDA-on-WSL guide explains how to run CUDA workloads in WSL. (NVIDIA Docs)
- Microsoft also documents enabling NVIDIA CUDA on WSL. (Microsoft Learn)
If you use QLoRA-style memory savings, you’ll commonly run into bitsandbytes setup questions; its installation guide covers supported platforms and CUDA constraints. (Hugging Face)
The end-to-end workflow (the “how”)
Phase A — Decide what you will distribute (two viable options)
Option 1 (smallest download): distribute an adapter
- Recipients pull the base model separately.
- You ship: adapter + Modelfile.
Ollama’s documented Modelfile pattern:
FROM <base model name>
ADAPTER /path/to/adapter.gguf
(Ollama Documentation)
Pros
- Smaller file to share.
- Faster iteration.
Cons
- Not a single file (needs the base model too).
- Must match the base model exactly. (Ollama Documentation)
Option 2 (your stated goal): distribute a single merged GGUF
- You merge adapters into the base and export one GGUF.
- Recipients get exactly one
.gguf (plus a Modelfile, if needed).
Ollama Modelfile supports building from a GGUF file via FROM. (Ollama Documentation)
Pros
- Exactly “one file like an Ollama model.”
Cons
- Much larger download.
- More steps to build correctly.
Phase B — Create a training dataset from your material (most important step)
You want a dataset that teaches the model how to answer, not to reproduce entire documents.
Good training example types
- FAQ / Q&A: “Question” → “Answer”
- Grounded Q&A: Provide a short excerpt + question → answer that stays within the excerpt
- Summaries: “Summarize this section” → bullet points
- Extraction: “Extract definitions / key rules” → list
- Refusal behavior: “If the text doesn’t contain the answer, say you don’t know”
Why this matters
- This is how you reduce hallucinations without retrieval.
- It also lets you control tone and formatting.
Format
- Most trainers accept JSONL with instruction/response pairs (or chat-style
messages lists).
Phase C — Fine-tune with SFT + LoRA/QLoRA
Common beginner-friendly approach
- Use Hugging Face TRL SFTTrainer for supervised fine-tuning. (Hugging Face)
- Use PEFT LoRA to keep training lightweight. (Hugging Face)
At the end, you’ll have:
- a base model reference
- an adapter (LoRA weights)
- tokenizer/config
If you want a standalone model, merge the adapter:
- PEFT’s
merge_and_unload() produces a merged model you can export as a single unit. (Hugging Face)
Phase D — Convert to GGUF (for Ollama)
This is the standard “training format → inference format” bridge:
- llama.cpp provides
convert_hf_to_gguf.py and the community has step-by-step conversion tutorials. (GitHub)
Typical pattern
- Convert to a higher-precision GGUF first (F16/BF16/F32).
- Then quantize.
Phase E — Quantize the GGUF (so it runs well)
Quantization makes models smaller and faster at the cost of some accuracy.
- llama.cpp’s quantize tool takes a high-precision GGUF and converts it to a quantized GGUF. (GitHub)
- Ollama also documents quantizing FP16/FP32 models using
ollama create -q/--quantize. (Ollama Documentation)
For your 12GB VRAM, quantized 7B/8B-class models are usually the practical target for local inference (exact fit depends on model, context length, and runtime).
Phase F — Package in Ollama (what you actually share)
If you’re sharing one GGUF
-
Create a Modelfile:
FROM ./your-model.gguf
-
Build it:
If you’re sharing an adapter
Practical advice specific to your constraints
Choose the smallest base model that meets your quality bar
With 12GB VRAM, you’ll waste the least time by:
- Prototyping your dataset and prompts on a small model (1B–3B) first.
- Only then scaling to 7B/8B with LoRA/QLoRA.
This minimizes “I trained for days and learned my dataset was wrong.”
Major pitfalls (things that commonly derail people)
-
Expecting “many documents” accuracy from fine-tuning alone
Fine-tuning improves tendencies and common answers, but it is not a faithful document store.
-
Base model licensing / redistribution
If you plan to share publicly, you must ensure the base model license permits redistribution of derivatives, and your own content rights permit training and redistribution.
-
Adapter/base mismatch
Ollama explicitly warns adapters must match the base model they were created with, or results can be erratic. (Ollama Documentation)
-
Windows environment friction
If training becomes dependency-heavy, WSL2 + CUDA is the documented path to run NVIDIA CUDA workloads with Linux tooling. (NVIDIA Docs)
Minimal “roadmap” you can actually execute
- Pick a base model you can redistribute derivatives of.
- Create ~200–2,000 high-quality instruction examples (Q/A, grounded Q/A, summaries).
- Run SFT with LoRA/QLoRA (TRL + PEFT). (Hugging Face)
- If you want one file: merge (
merge_and_unload). (Hugging Face)
- Convert to GGUF (llama.cpp conversion). (Hugging Face)
- Quantize (llama.cpp quantize or
ollama create -q). (GitHub)
- Package via Modelfile and
ollama create. (Ollama Documentation)
If you want the most cost-effective sharing: distribute an adapter first (small), and only ship a full merged GGUF once you’re sure the result is worth a large download.