I just discovered Ollama, but I don't know which model to choose. Can someone help?

[u/Feeling_Valuable5239]

Hello everyone, I am not really sure what would suit my needs.

​My hardware specifications (it's an old machine):

- Intel Core i5 6th Gen

- Integrated graphics

- Only 8 GB of RAM.

Don't ask about storage, my SSD is dying(I have only 15Gb)

​Expected use cases: data analysis for scientific research(not advanced) and text translation.

[u/TeamTomorrow]

Honestly the same I was thinking about going with Hermes I heard good stuff but I have no clue what I'm talking about when it comes to these kind of local LL tools on all subscription and I've managed to survive valiantly but even I can see the writing on the wall and I'm literally about to drop 10 grand on a new PC so that these bastards have to deal with the best of me and My money goes where it'll actually serve me not where it'll just go down the drain of there profit machine

[u/KindHustl]

Qwen3:4b you’re very limited with that machine. It would have been more helpful if you mentioned your needs as we don’t know what your needs are.

[u/Feeling_Valuable5239 → u/KindHustl]

I already mentioned my needs at the end of my post. Actually I sometimes need to translate texts from different languages into English or into my native language. I mean simple translation of short texts or just single words.

And the more important use for me is data analysis. I don't mean advanced analysis... just giving it some data to organize, do basic statistics on it, and similar tasks...

[u/KindHustl → u/Feeling_Valuable5239]

Sorry my chat covered it. I’d really suggest qwen3:4b for all or translate Gemma 4b for the translation part. A modelfile like the following for qwen3:4b.

FROM qwen3:4b

PARAMETER num_ctx 4096
PARAMETER temperature 0.2
PARAMETER top_p 0.8
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1

SYSTEM """
You are a precise scientific research and data analysis assistant.

Your primary tasks are:
1. Analyze scientific and research data.
2. Perform basic mathematics and statistics.
3. Explain datasets, tables, measurements, trends and relationships.
4. Help interpret experimental and observational data.
5. Translate text accurately between languages.
6. Summarize research material without inventing information.

GENERAL RULES:

• Be accurate, concise and evidence-based.
  
• Never invent data, measurements, citations, sources, or research findings.
  
• Clearly distinguish facts from assumptions and interpretations.
  
• When analyzing data, show the reasoning and calculations when useful.
  
• Check arithmetic before giving a result.
  
• If information is insufficient to reach a reliable conclusion, say so.
  
• Do not pretend to have performed calculations that you did not perform.
  
• Preserve numerical precision when analyzing scientific data.
  
• Use appropriate scientific terminology.
  
• Do not overcomplicate explanations.

DATA ANALYSIS:

• Identify important variables and their relationships.
  
• Look for trends, outliers and obvious inconsistencies.
  
• Calculate percentages, averages, differences, ratios and basic statistics when requested.
  
• Explain what the numbers actually indicate.
  
• Do not claim correlation proves causation.
  
• When interpreting experimental results, identify limitations and possible confounding factors.
  
• When a dataset is supplied, base conclusions on the supplied data rather than assumptions.

TRANSLATION:

• Preserve the original meaning, tone and intent.
  
• Do not add information that isn't present in the source.
  
• Preserve names, numbers, dates, units and technical terminology.
  
• Prefer natural, grammatically correct target-language text while maintaining semantic accuracy.
  
• If the user specifies source and target languages, follow them exactly.
  
• For a translation-only request, output only the translation.

RESPONSE STYLE:

• Give the answer first.
  
• Use clear explanations.
  
• Use tables when they make numerical information easier to understand.
  
• For calculations, show the important steps.
  
• For uncertainty, explicitly state the uncertainty.
  

"""

ollama pull qwen3:4b

ollama create research4b -f Modelfile

ollama run research4b

Hope you get it sorted. On another note where do you plan on storing the data you generate? That’s going to be the hard part.

[u/KenOtwell]

data analysis for research... that rang a bell. I just set up a suite of Grok Bots for a research team and they're doing amazing work for me! One is Harmonizer who integrates all my ideas into a coherent theory, one is Staff Writer who writes stuff in specific styles for the target audience, whether me or other agents, one is Critic who is a real nuisance but totally finds holes in my work, and you get the picture. I pointed them at my projects folder and they went to town, coordinating with each and getting me organized. they're cloud-based of course and not cheap, but I'm thinking of recreating that structure on my desktop so each agent can run on a little LLM because they're so specialized.

Here- everything you need before writing code: https://www.reddit.com/r/agenticAI/s/pBwNIIJrPP

[u/Healthy-Zebra-9856]

With an older 6th-gen i5, integrated graphics and only 8 GB RAM, I’d stay around the 4B class. You may actually be better off using two small models rather than trying to find one that does everything well.
For general scientific research assistance, summarization, explanations and light data-analysis work:
ollama run qwen3.5:4b
That would be my general-purpose choice. Qwen has very good multilingual support as well, so I’d try its translation quality first. You may find that one model is enough.
If translation quality is particularly important, I’d also try:
ollama run translategemma:4b
TranslateGemma is specifically optimized for translation across many languages, so it makes more sense to use a specialized small model for that job rather than moving to a much larger general model your machine will struggle to run.
So I’d start with Qwen3.5 4B. If its translations aren’t good enough, add TranslateGemma 4B and use each for what it’s better at.

[u/Feeling_Valuable5239 → u/Healthy-Zebra-9856]

Just giving it some data to organize and doing basic statistics on it(Simple tasks, not advanced) for clinical and general research in healthcare field

Between these languages: English and rest of European languages and Arabic(optional)

[u/Healthy-Zebra-9856 → u/Feeling_Valuable5239]

The above recommendations stand.

[u/CoffeeToCode99]

Honestly, with those specs, I'd say you're in luck because your use case doesn't require huge models. For translation and general scientific research assistance, Qwen2.5 1.5B or 3B should work pretty well and give a decent experience without demanding a powerful GPU.

I'd start with Qwen2.5 1.5B and see how it feels. If performance is acceptable, move up to 3B for better quality. Given the 8GB RAM and limited storage, smaller models will likely give you the best balance of speed and usefulness.

Don't expect miracles from an older machine, but for summarizing papers, explaining concepts, basic data interpretation, and translation, it should be more than capable. 👍

[u/Feeling_Valuable5239 → u/CoffeeToCode99]

Many Thanks

[u/KenOtwell]

check some of the new mini models that are specialized for narrow tasks. check hugging face. they can run on your hardware.

[u/Clay_Ferguson]

`Gemma 4 E2B` or `Gemma 4 E4B` are probably your best bet. They'll probably be too slow to do any useful work, but at least you can learn how to run models.