Qwen2.5-Coder 1.5B (Ollama) is the smallest Qwen2.5-Coder size still worth wiring into an agent loop. The default Q4_K_M tag is a 986MB download that fits in roughly 3GB of VRAM or unified memory, the context is 32K tokens (32,768), and the pricing is Free (self-hosted). Inside Atlas it is a realistic small_model slot for title generation and summarization while a heavier model does the actual editing.
What can Qwen2.5-Coder 1.5B (Ollama) actually do in Atlas?
Qwen2.5-Coder 1.5B (Ollama) handles Atlas title generation and summarization at a 986MB download, which loads on an integrated GPU or plain CPU. Point small_model at ollama/qwen2.5-coder:1.5b and leave the model slot on something larger, so the 1.5B only touches cheap traffic.
Atlas fires a steady stream of small calls: session titles, summaries, compaction passes. Qwen2.5-Coder 1.5B (Ollama) is the cheapest coder-tuned model that answers those calls competently. Because it is a coder model rather than a general chat model, its summaries of diffs and code chunks read closer to what a developer would write. The setup Atlas documents is explicit: `ollama pull qwen2.5-coder:1.5b` (986MB), register it under the ollama provider, then point small_model at ollama/qwen2.5-coder:1.5b and leave model on something larger. That is the job. Nothing about the 1.5B suggests it should be doing more.
How much VRAM does Qwen2.5-Coder 1.5B need for Atlas?
Qwen2.5-Coder 1.5B (Ollama) fits in roughly 3GB of VRAM or unified memory on the default Q4_K_M tag, and that figure leaves room for the KV cache. At 986MB on disk, Qwen2.5-Coder 1.5B loads on an integrated GPU or plain CPU with no discrete card required.
The 986MB number is the download; roughly 3GB is what you should budget to serve it with headroom for the KV cache. Run `atlas device` to confirm the runtime sees your hardware: it reports whether Ollama is installed and whether a GPU is present. Ollama also ships the full quant matrix on this size, from q2_K through fp16, plus base and instruct variants, so you can trade quality for footprint without leaving the qwen2.5-coder:1.5b tag family. That matters on a laptop where the build model already owns most of the memory and the small_model has to squeeze into what is left.
Does Qwen2.5-Coder 1.5B have the same context window as the larger sizes?
Yes. Qwen2.5-Coder 1.5B (Ollama) carries 32K tokens (32,768) of context, the same window as every other Qwen2.5-Coder size including the 32B. Prompts written for the 32B therefore run unchanged on the 1.5B, and Atlas registers it with limit.context 32768 and limit.output 4096.
The identical 32K window across the Qwen2.5-Coder line is a genuine operational convenience. You can prototype an Atlas prompt or a subagent definition against the 1.5B on a laptop, then swap the tag for a larger Qwen2.5-Coder size on a workstation without rewriting a single context budget. What does not carry over is judgment. The window is the same; the reasoning behind it is not. A 32K prompt that the 32B navigates confidently will overwhelm the 1.5B, so keep the small_model calls short even though the window technically allows more.
Why should Qwen2.5-Coder 1.5B never be the Atlas build agent?
A 1.5B model will lose the thread on multi-file refactors. Qwen2.5-Coder 1.5B (Ollama) is the cheap slot, not the build agent. Instruction following also degrades under long tool-call chains, so keep Atlas permissions on ask rather than allow when the 1.5B is anywhere in the loop.
Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, which is exactly the safety valve you want with a small model. Setting permissions to ask means a malformed or ill-judged tool call from Qwen2.5-Coder 1.5B (Ollama) stops at a prompt instead of touching the repo. Atlas also computes a unified diff for every file edit and surfaces it for approval before writing, giving you a second checkpoint. Both guardrails exist regardless of model, but with a 1.5B in the loop they stop being optional hygiene and start being load-bearing.
When should you move up from Qwen2.5-Coder 1.5B?
Move up from Qwen2.5-Coder 1.5B (Ollama) the moment Atlas needs to hold a multi-file refactor in its head. A 1.5B model loses the thread on those. Keep the 986MB tag registered as small_model, and put a larger Qwen2.5-Coder size in the model slot to do the editing.
Qwen2.5-Coder 1.5B (Ollama) is Free (self-hosted) at any token volume, so there is no cost pressure to retire it. The right move is to keep it and add a bigger model beside it. Atlas lets you switch the active model and provider on the fly with favorites and recents, so a laptop session can run everything on the 1.5B while a desk session promotes a larger tag into the model slot. Run `atlas models ollama` to verify resolution, then index the repo with Ollama embeddings so both the code and the vectors stay on the machine.
Setup
- 01Install Ollama, then run: ollama pull qwen2.5-coder:1.5b (986MB).
- 02Confirm the runtime sees your hardware with: atlas device (it reports whether Ollama is installed and whether a GPU is present).
- 03In atlas.json add an ollama provider whose npm field is @ai-sdk/openai-compatible and whose options.baseURL is http://localhost:11434/v1.
- 04Register the model key qwen2.5-coder:1.5b with limit.context 32768 and limit.output 4096.
- 05Point small_model at ollama/qwen2.5-coder:1.5b and leave model on something larger, so the 1.5B only handles titles and summaries.
- 06Run atlas models ollama to verify resolution, then index the repo with Ollama embeddings so both the code and the vectors stay on the machine.
- 07Keep Atlas permissions on ask rather than allow, because instruction following degrades under long tool-call chains at 1.5B.
Frequently asked questions
- how big is qwen2.5-coder 1.5b in ollama
- Qwen2.5-Coder 1.5B (Ollama) is a 986MB download on the default Q4_K_M tag, and it fits in roughly 3GB of VRAM or unified memory with room left for the KV cache. Pull it with: ollama pull qwen2.5-coder:1.5b.
- what context window does qwen2.5-coder 1.5b support
- Qwen2.5-Coder 1.5B (Ollama) supports 32K tokens (32,768), the same context as every other Qwen2.5-Coder size, so prompts written for the 32B run unchanged on the 1.5B. Register it in atlas.json with limit.context 32768 and limit.output 4096.
- can qwen2.5-coder 1.5b do multi-file refactors
- No. A 1.5B model will lose the thread on multi-file refactors. Treat Qwen2.5-Coder 1.5B (Ollama) as the cheap small_model slot for Atlas titles and summaries, and give the model slot to something larger.
- how do i configure ollama as a provider in atlas.json
- Add an ollama provider whose npm field is @ai-sdk/openai-compatible and whose options.baseURL is http://localhost:11434/v1, then register the model key qwen2.5-coder:1.5b under its models map. Verify with: atlas models ollama.
- what does atlas device report
- Running atlas device reports whether Ollama is installed and whether a GPU is present. Run it before pulling Qwen2.5-Coder 1.5B (Ollama) so you know whether the 986MB tag will land on an integrated GPU, a discrete card, or plain CPU.
- which quantization should i use for qwen2.5-coder 1.5b
- The default tag is Q4_K_M at 986MB. Ollama ships the full quant matrix on this size, q2_K through fp16, plus base and instruct variants, so you can trade quality for footprint without leaving the qwen2.5-coder:1.5b tag family.
- is qwen2.5-coder 1.5b free to use
- Yes. Qwen2.5-Coder 1.5B (Ollama) is Free (self-hosted). After the 986MB pull there is no per-token charge, which is what makes it a realistic permanent small_model slot for Atlas title generation and summarization.
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