LOADING DATASHEET
LOADING DATASHEET
by Alibaba
RANKED #275 OF 316 ASSISTANTS · OVERALL #427 OF 493 RANKED · VIBE SCORE 5.2 · 112 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit on Hugging Face (text generation). 119,124 downloads. Open weights for local or hosted use.
7 mentions
8 mentions
26 weeks · 127 voices in this window
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download Qwen2.5 1.5B Instruct once and it's yours. No subscription, no rate limits, works offline.
1.54B parameters, a Q4_K_M file is about 986 MB (full FP16 is around 3.1 GB)
Raspberry Pi 5 (8GB) or budget laptop CPU
8 GB RAM
~$80Q4_K_M on CPU, ~12 tok/s, 8k context
NVIDIA GeForce GTX 1650 4GB
4 GB VRAM
~$85 USEDQ8_0 or FP16 fully offloaded to GPU, ~60 tok/s, 32k context
NVIDIA GeForce RTX 3060 12GB
12 GB VRAM
~$260 USEDUnquantized FP16 with full 128k context window offloaded to VRAM, ~140 tok/s
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long Qwen2.5 1.5B Instruct takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMRaspberry Pi 5 (8GB) or budget laptop CPU | SWEET SPOTNVIDIA GeForce GTX 1650 4GB | FULL POWERNVIDIA GeForce RTX 3060 12GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~42 S★★★★★ | ~8 S★★★★★ | ~4 S★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~2.1 MIN★★★★★ | ~25 S★★★★★ | ~11 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~11 MIN★★★★★ | ~2.2 MIN★★★★★ | ~57 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~35 MIN★★★★★ | ~6.9 MIN★★★★★ | ~3 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~21 MIN★★★★★ | ~4.2 MIN★★★★★ | ~1.8 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
People are mostly frustrated with Qwen2.5 1.5B Instruct right now. Biggest gripe: saying no too much.
104 POSTS · 8 COMMENTS · REDDIT · GITHUB · DEV FORUMS · DEV.TO
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Other models the crowd has fully reviewed, starting with text models like this one.
[P] I trained Qwen2.5-1.5b with RLVR (GRPO) vs SFT and compared benchmark performance
OrangePi Zero 3 runs Ollama
I trained a 1.5B model to be less overconfident instead of pretending uncertainty doesn't exist
So many models...confused how to pick the right one. Need one to help fix English grammar and text.
Help with qwen and Heretic
fix(ollama): reconstruct streaming tool calls, indices, finish_reason and error chunks
Looking for Windows users with 2–8 GB GPUs to test a low-VRAM local LLM runtime
Spike: Android llama.cpp GGUF constrained meal planning
[Research/Project] Controlling LLM Logic via Deterministic Steering Kernels: A Live Matrix 🩻 "X-Ray" of Qwen2.5-1.5B (Test 73)
fix(ali): require channel tariffs for trial and legacy Qwen models
engine-fix(oMLX): doubled < begin_of_text > on Llama-3 chat requests (238/195/91 prompt tokens): every Llama-3 chat is corrupted
Vllm推理问题,推理结果完全不对