Local AI Advisor

JustRL-II-base-model on local hardware

Estimated comfort for repository coding with Aider on a MacBook Pro M4 Pro 48GB.

JustRL-II-base-model

openbmb · See model card · 752 downloads

Hugging Face
Reasoning / thinking mode
Parameters
2.5B estimated
Context window
64K tokens
Attention
Standard (full attention)
Layers · KV heads · head dim
42 · 2 · 128
KV cache
42 KB/token · 1.3 GB at 32K
Tool calling
good (from chat template)
Thinking
good (from chat template)
Vision
basic (inferred — no supporting signal)
Commercial use
check cardCheck the model card
Import confidence
low · capabilities estimated

Weights by quantization

Q3_K_M

1.2 GB

computed · low confidence

Q4_K_M

1.5 GB

computed · low confidence

Q5_K_M

1.7 GB

computed · low confidence

Q6_K

2.0 GB

computed · low confidence

Q8_0

2.6 GB

computed · low confidence

F16 / BF16

4.8 GB

computed · low confidence

  • • Parameter count estimated from the architecture (the Hub reports none for this repo).
  • • Assumes GGUF / MLX conversions of this model exist (they do for most popular models). Sizes are computed from bits per weight.
  • • vision: basic, inferred from missing signals (low confidence).
  • • Capability ratings are estimated from model size and name — this model hasn't been assessed by hand.
  • • Commercial use unknown: Check the model card.

JustRL-II-base-model — 6-bit

Technically Runs for your workload

Technically Runs

Speed

60–80 tok/s

Excellent

Memory left

32 GB

of 48 GB

First word in

≈15 s

Excellent

All the details
Your setup
MacBook Pro M4 Pro 48 GB
Workload
Repository coding with Aider
Runtime → tool
LM Studio → Aider
Memory used
4.2 GB
Context
32K · up to 64K practical
Tool support
Official
Use-case fit
Poorly suited

Why?

  • Model fits with approximately 32 GB of memory headroom.
  • LM Studio supports hardware acceleration on this machine (Metal (Apple GPU)).
  • Aider can connect to LM Studio (officially supported).
  • Generation (60–80 tok/s) should be appropriate for interactive coding.
  • Ingesting a prompt of 14K tokens takes ≈15 s; prompt processing keeps up with interactive coding.
  • 32K working context is reasonable.
  • Weak coding capability for interactive coding.

Why “Technically Runs” rather than “Borderline”?

  • Workload suitability: Weak coding capability for interactive coding.

How sure are we?

Medium confidence

Estimated from bandwidth and compute, calibrated against 2 verified benchmarks of other models on this same chip (measured with a different engine, so calibration is partial).

  • Calibrated with verified benchmarks of other models on this chip.
  • Some specifications come from lower-confidence sources.
Medium confidence
Check with your hardware and workload