Metadata-Version: 2.4
Name: ggufit
Version: 0.1.3
Summary: Check whether a local LLM can run on your machine (CPU-only inference).
Author: Mouad
License: Ggufit Use-Only License
        
        Copyright (c) 2026 Mouad
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to use
        and run the Software for any purpose, including commercial purposes, and to
        redistribute unmodified copies of the Software, subject to the following
        conditions:
        
        1. The above copyright notice and this permission notice shall be included
           in all copies of the Software.
        
        2. You may NOT modify, adapt, translate, or create derivative works based
           on the Software.
        
        3. You may NOT redistribute the Software, or any portion of it, in modified
           form.
        
        4. You may NOT sublicense the Software under different terms.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
        ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
        WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Utilities
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

<div align="center">

# ggufit

### Will this LLM actually run on my machine?

Answer it in two seconds, before downloading 40 GB to find out.

[![PyPI](https://img.shields.io/pypi/v/ggufit)](https://pypi.org/project/ggufit/)
[![Python](https://img.shields.io/pypi/pyversions/ggufit)](https://pypi.org/project/ggufit/)
![Dependencies](https://img.shields.io/badge/dependencies-none-brightgreen)
![Platform](https://img.shields.io/badge/platform-Linux%20%7C%20macOS%20%7C%20Windows-lightgrey)
![Models](https://img.shields.io/badge/models-218-blue)

</div>

---

`ggufit` sizes local LLMs against **your** hardware: it measures your real memory bandwidth,
computes each model's true memory footprint from its actual architecture, and tells you what
fits and roughly how fast it will generate.

It is CPU-only by design, has **zero dependencies**, and makes **no network calls** — the
model catalogue ships with it.

```bash
pipx install ggufit     # recommended: isolated env, global `ggufit` command
pip install ggufit      # or inside a venv
```

## See it work

```console
$ ggufit scan --for code --fastest --top 4

=== Hardware Profile ===
  OS:              Linux 6.2.0-39-generic
  Logical cores:   8
  RAM total:       7.44 GB
  RAM free now:    1.96 GB
  Measured bandwidth: 17.5 GB/s (multi-process, real)

=== Models For This Machine (code) ===
  With RAM cleared: 5.44 GB usable (everything but 2 GB reserved for the OS)
  Right now: 1.96 GB free, with what you have open left alone
  Sorted by estimated speed, quickest first.

  Runs right now (2):
    - Qwen2.5-Coder 0.5B  [Q8]  ~21.6-40.0 tok/s
    - Qwen2.5-Coder 1.5B  [Q3]  ~14.5-26.9 tok/s

  Usable if you free up RAM (12):
    - CodeGemma 2B  [Q8]  ~4.2-7.9 tok/s
    - DeepSeek-Coder 1.3B  [FP16]  ~4.1-7.7 tok/s
      ... and 10 more (raise --top to see them)

  Will not fit on this machine (17):
    - Code Llama 13B
    - Code Llama 34B
      ... and 15 more (raise --top to see them)
```

Or drill into one model:

```console
$ ggufit llama3.1

=== Llama 3.1 8B @ 4096 ctx ===
  Right now:        does not fit
      Needs 4.72 GB at Q2, only 2.01 GB is free.

  With RAM cleared: runs at Q2   <- free up RAM and this becomes usable
      Model size: 2.99 GB
      KV cache:   0.5 GB
      Total need: 4.72 GB of 5.44 GB
      Speed:      ~3.6-6.8 tok/s  (memory-bandwidth bound, uncalibrated +/-30% range)
      Note: Q3 would need 5.52 GB, short by 0.08 GB.
```

## Why not just multiply parameters by bytes?

Because that gets three things wrong, and each one is worth gigabytes:

| | The naive answer | What `ggufit` does |
|---|---|---|
| **Attention cache** | assume every model caches per attention head | reads each model's real K/V head counts — a **4-8x** difference on modern models |
| **Mixture-of-experts** | score a 671B model as if all 671B are read per token | fits on total weights, but times on **active** experts only |
| **Memory bandwidth** | look up the RAM spec sheet | **measures** it, multi-process, on your machine |

Concretely: Llama 2 70B at 32K context needs **10 GB** of attention cache, not the 80 GB a
per-head assumption gives you. That is the difference between "you need a server" and "this
runs on a workstation."

Mamba and DeepSeek's MLA are handled as themselves too — a pure state-space model has *no*
attention cache at any context length, and MLA caches one compressed vector per layer.

## Two answers, not one

Every result is reported against **two** memory figures, because "can this machine run it"
and "can it run it without closing anything first" are different questions, and one number
quietly conflated them:

- **Right now** — what is actually free at this instant, with everything you have open left
  alone. A live reading, so it moves between runs.
- **With RAM cleared** — total installed RAM minus a reserve for the OS (2 GB or 15%,
  whichever is larger). Fixed and reproducible: the machine's real ceiling.

That is why `scan` has a middle group. **Usable if you free up RAM** means the model fits
your hardware fine — just not alongside your browser. Where free memory cannot be read, the
two groups collapse into one rather than guessing.

## Usage

```bash
# What runs on this machine
ggufit scan

# Filter by what you want the model for
ggufit scan --for code          # also: reasoning, math, vision, chat, general

# Sort by speed, cap the output
ggufit scan --fastest
ggufit scan --for code --fastest --top 5

# Drill into one model (short names, HF-style and Ollama-style all work)
ggufit llama3.1
ggufit llama-3.1-8b
ggufit llama3.1:8b

# Force a quant level, or test a longer context
ggufit llama3.1 --quant q4
ggufit qwen2.5-32b --context 16384
```

Output is plain text when piped (`ggufit scan > report.txt`), coloured when it is a
terminal. `NO_COLOR` and `FORCE_COLOR` are both honoured.

### Installing from source

```bash
cd ggufit/                # the folder containing pyproject.toml
pipx install -e .         # editable install
# or: pip install -e .    (inside a venv, or with --break-system-packages)
```

## How it works

For each model:
1. **Model size (RAM)** = total params × bytes-per-parameter (varies by quant: FP16, Q8, Q6, Q5, Q4, Q3, Q2)
2. **KV cache** uses the model's real attention architecture, not a flat guess:
   - **Standard / GQA models**: `2 × layers × num_kv_heads × head_dim × context_len × bytes_per_elem`,
     using each model's *actual* KV head count (sourced from its Hugging Face `config.json`), not
     `hidden_size`. Most models since Llama 2 70B use Grouped-Query Attention, where `num_kv_heads`
     is far smaller than the number of query heads — using `hidden_size` there over-estimates the
     KV cache by 4-8x (e.g. Llama 2 70B at 32K context: 10 GB real vs 80 GB under the old formula).
   - **MLA** (DeepSeek-V2/V3/R1, MiniCPM3): `layers × (kv_lora_rank + qk_rope_head_dim) × context_len
     × bytes_per_elem` — MLA caches one compressed latent vector per layer, not one per head.
   - **Pure SSM/Mamba** (no attention at all): `0` — a fixed-size recurrent state that doesn't grow
     with context length.
   - A handful of older/override-only models have no published head-count data; those still fall
     back to the conservative `hidden_size`-based estimate.
3. **Total RAM needed** = `weights × overhead_multiplier(weights) + KV cache`
   - The overhead multiplier accounts for compute buffers, activations, and allocator
     overhead. It is **size-dependent**, not a flat percentage: a fixed ~1GB baseline
     buffer plus ~7.6% of weight size. This means it's high for tiny models (~2x for a
     1GB model, which genuinely needs ~1GB of buffers on top) but asymptotes to ~1.08x
     for very large models. Calibrated against real-world llama.cpp memory reports across
     7B/13B/70B/671B models — e.g. DeepSeek-V3 671B at Q4 comes out to ~405GB, matching
     reality, instead of the ~450GB an old flat-20% rule would have predicted.
4. **RAM budget** — two of them, see "Two answers, not one" above. The reproducible one is total
   installed RAM minus `max(2 GB, 15% of total)` reserved for the OS; the live one is whatever is
   free at this instant. The reproducible figure is what the fit verdict is anchored to, since a
   live reading alone used to make `ggufit` report a different verdict for the same machine seconds
   apart.
5. **Speed estimate** = `measured_memory_bandwidth / active_size`, shown as a range (±30%, since the
   runtime-efficiency constant behind it is not yet calibrated against real measurements — see
   Known limitations). `active_size` is the total model size for dense models, or just the active
   experts' size for MoE models (`active_params_billion`) — since only those weights are streamed
   from RAM per token.

`ggufit` runs a real memory-bandwidth benchmark (several worker processes copying a buffer
concurrently, STREAM-style) instead of guessing from RAM specs, since achievable bandwidth depends
heavily on channel configuration. It has no dependencies — standard library only.

## Model catalogue

The 218-model catalogue (`ggufit/models.json`) is generated from HuggingFace `config.json` /
safetensors metadata, not hand-typed — see the `ggufit` repo's `tools/fetch_model_specs.py`. This is
what makes the per-model KV head counts above trustworthy: a hand-maintained file couldn't carry that
data without it silently going stale. A few fields (MoE `active_params_billion`, and specs for
repos HuggingFace can't resolve directly) come from a small manual overrides file instead, since
those genuinely aren't derivable from a config.

## Known limitations

- Speed estimates assume batch size 1, single-user chat, and are shown as an uncalibrated ±30% range
  — the runtime-efficiency constant behind them is a placeholder pending real tokens/sec measurements.
- Quant byte-per-param values are approximations of real GGUF file sizes.
- A handful of older or override-only models have no published attention head-count data and fall
  back to the conservative `hidden_size`-based KV estimate (over-estimates, never under-estimates).
- GPU/VRAM is not modeled at all — every figure is CPU-only. For a machine with a GPU, these numbers
  don't reflect what you'd actually get running on it.
- The overhead multiplier is calibrated against a handful of real-world data points
  (7B/13B/70B/671B at Q4); it's a strong approximation but not exact for every model/runtime.
- Prompt processing time isn't modeled — only generation speed.

## Changelog

Upgrading from 0.1.2? `UPDATE.md`, shipped in the source distribution, covers what changed
and why, the measured before/after figures, and the library-API breaking changes.

- **0.1.3** — KV cache now uses each model's real attention head counts (GQA) or MLA-specific fields
  instead of a flat `hidden_size` estimate, fixing a 4-8x over-estimation on most modern models
  (e.g. Llama 2 70B at 32K context: 80 GB → 10 GB, the correct figure). The memory-bandwidth
  benchmark is now a real multi-process STREAM-style measurement instead of a single-threaded numpy
  copy that read a fraction of true bandwidth. Results are now reported against two RAM figures —
  what's free right now and the machine's ceiling with RAM cleared — with a new **Usable if you free
  up RAM** group in `scan` for models that fit the hardware but not the current moment. New
  `--for <use>` filter (code, reasoning, math, vision, chat, general), `--fastest` sort and `--top N`
  limit. Speed is now shown as a range, not a false-precision single number. Dropped the
  `psutil`/`numpy` dependencies — the CLI is standard-library only now. Model catalogue expanded from
  217 hand-typed entries to 218 generated from HuggingFace.
- **0.1.2** — Overhead is now size-dependent (fixed ~1GB baseline + ~7.6% of weights),
  calibrated against real llama.cpp memory reports. Fixes large-model RAM over-estimation
  (DeepSeek-V3 671B Q4 now estimates ~405GB, matching reality, vs ~450GB before). Also fixes
  a wording bug where the "try a lower quant" hint could suggest the same quant that just failed.
- **0.1.1** — Custom use-only license; corrected install instructions.
- **0.1.0** — Initial release (MoE active-param speed, SSM/MLA KV cache handling, 217 models).

## License

`ggufit` uses a custom **use-only** license, not an open-source one. You may install and
run it for any purpose including commercial use, and redistribute unmodified copies. You
may **not** modify it, redistribute modified versions, or sublicense it. Full terms in the
`LICENSE` file.

Version 0.1.0 was published under MIT; that release stays MIT for anyone who already has
it. License changes apply going forward, not retroactively.

## Q&A

A comprehensive FAQ — from "what is a GGUF" to the exact formulas behind every number
`ggufit` prints. Organized beginner → expert.

### Basics

**What is a GGUF?**
GGUF (GPT-Generated Unified Format) is a file format for storing LLM weights, designed
by the llama.cpp project for fast loading and CPU/GPU-flexible inference. It bundles
the model's tensors and metadata (architecture, tokenizer, etc.) into a single file.
It replaced the older GGML format. If you've downloaded a `.gguf` file from Hugging
Face to run in `llama.cpp`, Ollama, or LM Studio, that's what `ggufit` is estimating
compatibility for.

**What is quantization?**
Shrinking a model's weights from their original precision (usually 16-bit floats) down
to smaller representations (8-bit, 4-bit, etc.) to save memory and speed up inference,
at the cost of some accuracy. A "Q4" model uses roughly a quarter of the memory of the
same model at full 16-bit precision.

**What do Q4, Q5, Q6, Q8 mean?**
The number is roughly the average bits per weight after quantization (not exactly,
see next question). Lower number = smaller file, faster inference, more quality loss.
Q4 is the most common "sweet spot" for CPU-only local inference. Q8 is close to
lossless but nearly as large as full precision.

**Why isn't Q4 exactly 4 bits then?**
Modern GGUF "K-quants" (the `_K_M`, `_K_S` suffixes you see on Hugging Face) don't use
a uniform bit-width across the whole model — they mix precision per tensor, using
slightly higher precision for the parts most sensitive to quality loss. So "Q4" is
really an *average* around 4-5 bits/weight in practice. `ggufit` uses effective
per-quant byte values that reflect this real-world average, not naive N-bit math:

| Quant | Effective bytes/param |
|---|---|
| FP32 | 4.0 |
| FP16/BF16 | 2.0 |
| Q8 | 1.05 |
| Q6 | 0.8 |
| Q5 | 0.7 |
| Q4 | 0.6 |
| Q3 | 0.5 |
| Q2 | 0.4 |

**What's the difference between RAM and VRAM, and why does ggufit only care about RAM?**
VRAM is memory on a dedicated GPU; RAM is your system's main memory, used by the CPU.
`ggufit` is CPU-only by design — it answers "can my CPU and system RAM handle this,"
not "can my GPU handle this." If you have a GPU, tools like `nvidia-smi` and the model
card's VRAM requirements are what you want instead.

**What is context length?**
The number of tokens (roughly, chunks of a word) the model can "see" at once —
your prompt plus its response so far. Longer context means the model can process
longer documents, conversations, and code, but it also means more memory used for
the KV cache (see below).

**What are tokens/sec, and what's a "good" number?**
How many tokens the model generates per second. For a comfortable reading pace, most
people find 5-15 tok/s tolerable for chat; below ~2 tok/s feels quite slow; above 20
tok/s feels close to instant. It's highly subjective and task-dependent though —
background batch jobs can tolerate much lower throughput than an interactive chat.

---

### Installing and running ggufit

**How do I install it?**
```bash
pipx install ggufit    # recommended
pip install ggufit     # inside a venv, or with --break-system-packages
```

**Why do I get "externally-managed-environment"?**
Modern Debian/Ubuntu (PEP 668) blocks system-wide `pip install` to protect the OS's
own Python packages. Use `pipx` instead — it installs into an isolated environment
while still giving you a global command.

**Why does `ggufit` say "command not found" right after installing?**
Either your venv isn't activated (`source venv/bin/activate`), or `pipx`'s bin
directory isn't on your PATH yet (run `pipx ensurepath` and reopen your terminal).

**Why did a model that should fit show as "does not fit"?**
Check which of the two verdicts you're reading. "Right now" reflects what's free at this
instant, so other programs holding memory will fail it; "with RAM cleared" is the machine's
real ceiling. A model in the **Usable if you free up RAM** group fits your hardware fine —
it just doesn't fit alongside what you currently have open.

**Can I check a model at a specific quant instead of letting ggufit auto-pick?**
Yes — `ggufit <model> --quant q4`. Without `--quant`, it auto-picks the highest-quality
quant that fits.

**How do I find models for a specific job, or just the fastest ones?**
`ggufit scan --for code` filters to coding models; `--for` also takes `reasoning`, `math`,
`vision`, `chat` and `general`. Add `--fastest` to sort by estimated speed and `--top N`
to cap the list, e.g. `ggufit scan --for code --fastest --top 5`. Tags are editorial
groupings held in `tools/aliases.json`, not something the model files declare, so treat
them as a helpful index rather than an authoritative capability list — plenty of
"general" models write perfectly good code.

---

### Understanding the numbers (intermediate)

**How is "Model size (RAM)" calculated?**
```
model_size_bytes = num_parameters × bytes_per_param
```
`bytes_per_param` comes from the quant table above. This is the number of bytes the
weights occupy once loaded — the dominant factor in "will this even load."

**What is the KV cache, and why does it matter?**
During generation, the model caches the Key and Value tensors from every previous
token in the conversation so it doesn't have to recompute them each step. This cache
grows with context length. The standard formula:
```
kv_cache_bytes = 2 × num_layers × hidden_size × seq_len × batch_size × bytes_per_param
```
The `2×` covers storing both K and V. This is why a model that fits fine at a 4K
context can stop fitting at 32K — the KV cache scales linearly with context length
while the model weights stay fixed.

**Why is there an "overhead factor" on top of model size + KV cache?**
Real inference isn't just raw tensor storage — the runtime, OS, and memory allocator
all need working space too (buffers, fragmentation, temporary activations). It's a
fixed ~1GB baseline plus ~7.6% of weight size (not a flat multiplier — see "How it
works" above), calibrated against real llama.cpp memory reports.

**Why is CPU inference speed based on memory bandwidth instead of raw compute (FLOPS)?**
At batch size 1 (the normal case for a single person chatting), generating each new
token requires reading every single model weight from RAM once. The CPU spends far
more time waiting on memory than doing arithmetic — so the bottleneck is how fast
data can move from RAM to the CPU, not how many operations per second the CPU can do.
That's why the formula is:
```
tokens_per_sec ≈ memory_bandwidth (GB/s) / model_size (GB)
```

**Why does ggufit benchmark my memory bandwidth instead of just knowing it from my RAM specs?**
Achievable bandwidth depends on RAM generation, channel configuration
(single/dual/quad-channel), and platform quirks — none of which are reliably
detectable across Windows/Mac/Linux without vendor-specific tools. So `ggufit` runs
several worker processes copying a buffer concurrently on your actual machine, right
now (a single thread can't saturate a memory controller multiple cores can drive), and
uses that measured number instead of guessing.

**Why does the bandwidth number change slightly every time I run ggufit?**
It's a live micro-benchmark, not a cached constant — normal system load, thermal
throttling, and other processes competing for memory access all cause small
run-to-run variance. That's expected and not a bug.

---

### Architecture-specific math (expert / advanced)

**What is a Mixture-of-Experts (MoE) model, and why does it need special handling?**
An MoE model has many "expert" sub-networks, but only a subset of them are activated
for any given token (a small router network decides which experts to use). This means:
- **All experts must be loaded into RAM** — because any token could route to any
  expert, so the *fit* check uses the full `params_billion` (total, all experts).
- **Only the active experts are actually read from RAM per token** — so the *speed*
  estimate uses `active_params_billion` instead. Using total params for speed
  would make big MoE models look absurdly slow (DeepSeek-V3 at 671B total but only
  37B active would look ~18x slower than it really is if you used the total).

```
fit check:   uses params_billion (total) — all experts must be resident
speed check: uses active_params_billion — only active experts are read per token
```

**What is GQA (Grouped-Query Attention), and how does ggufit account for it?**
Most modern transformer models don't give every attention head its own K/V
projection — they group multiple query heads to share a smaller number of K/V heads
(`num_kv_heads < num_attention_heads`). This makes the *real* KV cache smaller than
a formula assuming `hidden_size` worth of K/V per layer would predict — over-estimating
by 4-8x for models like Llama 2 70B or Mistral 7B. `ggufit` uses each model's actual
`num_key_value_heads` and `head_dim` (sourced from its Hugging Face `config.json`) to
compute the real KV cache size, not a blanket correction factor — that matters because
some models (Phi-3-mini) are genuinely full multi-head attention, and a global
multiplier would break those while fixing everything else. Only a handful of older or
override-only models still fall back to the conservative `hidden_size` estimate,
because no published head-count data exists for them.

**What is MLA (Multi-head Latent Attention)?**
MLA (used by DeepSeek-V2/V3/R1 and MiniCPM3) compresses the K/V representations into
a much smaller latent vector before caching them, then reconstructs full K/V on the
fly. `ggufit` computes this directly from the model's real `kv_lora_rank` and
`qk_rope_head_dim` fields — one compressed latent vector cached per layer, not one
per head — rather than applying an approximate correction factor.

**Why do pure Mamba/SSM models show a 0 GB KV cache?**
State-Space Models (Mamba, used in Codestral Mamba and Falcon-Mamba) don't use
attention at all — they maintain a fixed-size recurrent state instead of caching
every previous token's K/V. That state doesn't grow with context length. So their
KV cache is genuinely zero, regardless of how long the context gets. This is why
`ggufit falcon-mamba --context 65536` still shows ~0GB KV cache even at a huge
context length.

**What about hybrid models like Jamba?**
Jamba interleaves Mamba blocks with regular attention blocks — only a fraction of
layers actually use attention. `ggufit` doesn't yet have published per-layer
attention-head data for these hybrid architectures, so they fall back to the
conservative `hidden_size`-based estimate rather than a wrong precise-looking number.

**Why does `ggufit` need real `layers` and `hidden_size` values instead of estimating them from param count?**
Because two models with the same parameter count can have very different KV cache
sizes depending on how those parameters are distributed across layers and hidden
dimension — there's no reliable shortcut from param count alone. `ggufit` stores
these values explicitly per model (sourced from each model's Hugging Face
`config.json`) to keep the KV cache estimate accurate.

---

### Project & contributing

**How do I add a model that isn't in the database?**
The catalogue (`ggufit/models.json`) is generated, not hand-edited — see the `ggufit`
repo's `tools/fetch_model_specs.py`. Add the model's HuggingFace repo id to
`tools/repos.txt`, re-run the fetcher, and (for MoE models, or repos HuggingFace can't
resolve directly) add an entry to `data/overrides.json` for whatever the fetcher
couldn't derive on its own — most commonly `active_params_billion`.

**Why was the project renamed from moscan to ggufit?**
The name `moscan` was already taken on PyPI. `ggufit` was chosen instead — pun on
GGUF (the file format) + "fit" (does the model fit on your machine).

**What license is ggufit under?**
A custom "use-only" license: you're free to install and run it for any purpose,
including commercial use, but you may not modify it or redistribute a modified
version. See the `LICENSE` file for exact terms. Note: versions published before
this license was adopted (0.1.0) remain under their original MIT terms for anyone
who already obtained that specific release — license changes only apply going
forward, not retroactively.

**Is this a substitute for actually running the model to see how it performs?**
No — treat every number here as an estimate to guide a decision (e.g. "should I even
attempt downloading this 40GB file"), not a guarantee. Real-world speed depends on
your specific inference engine (llama.cpp, Ollama, etc.), thread count settings, OS
scheduler behavior, and quantization implementation quality, none of which `ggufit`
can measure without you actually running the model.
