Dataset card

README.md

@@ -0,0 +1,128 @@
+---
+license: cc-by-4.0
+pretty_name: Local Model Explorer Data
+language:
+  - en
+tags:
+  - gguf
+  - llama.cpp
+  - local-llm
+  - benchmark
+  - hardware
+size_categories:
+  - n<1K
+configs:
+  - config_name: events
+    data_files:
+      - split: train
+        path:
+          - data/events/*.parquet
+          - data/incoming/**/*.parquet
+    default: true
+---
+
+# Local Model Explorer Data
+
+An anonymous record of what people try to run locally, gathered by [Local Model Explorer](https://huggingface.co/spaces/LocalLLaMA/local-model-explorer): the hardware they plan for (GPU memory, number of cards, system or unified memory), the models and context lengths they look at, which GGUF quants they open and copy commands for, and the `llama-bench` results and reports they choose to share.
+
+The question it answers: **what hardware do local LLM users have, what do they try to run on it, and how fast does it actually go?**
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+df = load_dataset("LocalLLaMA/local-model-explorer-data", "events", split="train").to_pandas()
+df = df[df.suspicious_flags.apply(len) == 0]                      # clean rows only
+
+# One row per visit: keep the newest summary of each session
+visits = (df[df.event_type == "session"].sort_values("timestamp")
+            .drop_duplicates("session_id", keep="last"))
+visits.vram_class.value_counts()
+
+# Community llama-bench results: generation speed per model, quant and GPU
+bench = df[df.event_type == "llama_bench"]
+bench.groupby(["selected_model", "quant", "gpu_name"]).generation_tps.median()
+```
+
+## What is recorded
+
+The dataset records outcomes, not clicks.
+
+| `event_type` | One row per | Contents |
+| --- | --- | --- |
+| `session` | visit | Hardware class, the filters the visit ended on, the top result and how it fit, and which models were opened, which repos were followed to the Hub and which run commands were copied. Re-sent as the visit changes: **keep the newest row per `session_id`.** |
+| `feedback` | report | "Have you run this model?" with the quant, runtime, placement and how it went |
+| `llama_bench` | benchmark | Numbers from a pasted `llama-bench` run, parsed by the Space |
+
+## Schema (`schema_version` 1)
+
+Every row has every column; columns that don't apply to an event type are null.
+
+| Column | Type | Rows | Description |
+| --- | --- | --- | --- |
+| `timestamp` | string | all | UTC, second precision |
+| `schema_version`, `app_version` | int, string | all | |
+| `event_type`, `session_id` | string | all | `session_id` is random per browser tab and discarded when it closes |
+| `hardware_kind` | string | all | `gpu`, `unified`, `cpu` |
+| `gpu_model` | string | all | A listed card (e.g. `NVIDIA RTX 3090`) or `custom` |
+| `gpu_count` | int | all | Cards |
+| `vram_class` | int | all | Total GPU memory rounded down to a class: 0, 4, 6, 8, 10, 12, 16, 20, 24, 32, 40, 48, 64, 80, 96, 128, 160, 192, 256, 320, 384, 512 GB |
+| `ram_class` | int | all | System (or unified) memory rounded down to a class |
+| `unified_system` | string | all | `Apple Silicon Mac`, `AMD Ryzen AI Max+ 395 (Strix Halo)`, `NVIDIA DGX Spark` |
+| `hardware_source` | string | all | `preset` or `custom` |
+| `webgpu_vendor` | string | session | WebGPU adapter vendor, e.g. `apple`, `nvidia` |
+| `browser_family`, `os_family` | string | session | Coarse families |
+| `model_family`, `size_bucket` | string | session, … | Final filters; on reports, the model's |
+| `context`, `kv_type` | int, string | session | Planned context length and KV cache precision (`f16`, `q8_0`, `q4_0`) |
+| `priority`, `sort`, `fits_only` | string, string, bool | session | |
+| `result_count`, `families_searched`, `distinct_queries`, `searched` | int, list, int, bool | session | What the visit looked at |
+| `top_model`, `top_quant`, `top_fit` | string | session | First result and its fit: `gpu`, `gpu_moe` (experts in RAM), `partial`, `cpu`, `unified`, `unified_tight`, `no` |
+| `engine_version` | string | session | Ranking engine, e.g. `fit-v1` |
+| `models_viewed`, `repos_clicked`, `commands_copied` | list | session | Opened models (max 10), repos followed (max 5), `llama.cpp` / `ollama` |
+| `selected_model`, `repo`, `quant` | string | reports | The model (base repo id, or a name for variants), the GGUF repo and quant |
+| `quant_bucket`, `params_b`, `moe`, `arch`, `repo_downloads_at_selection` | | reports | Filled in by the Space from the catalogue |
+| `tried`, `outcome`, `quality_rating`, `failure_reason` | string | feedback | `outcome` is `worked` or `problem` |
+| `runtime`, `placement` | string | feedback | `llama.cpp`, `ollama`, `lm-studio`, …; `gpu`, `experts_in_ram`, `partial`, `cpu`, `unified` |
+| `reported_tps`, `reported_context`, `notes` | float, int, string | feedback | Notes ≤ 280 characters; emails, links and phone numbers are removed |
+| `prompt_tokens`, `prompt_tps`, `generation_tokens`, `generation_tps` | int, float | llama_bench | From the pp and tg rows |
+| `backend`, `n_gpu_layers`, `n_threads`, `flash_attn`, `type_k` | | llama_bench | Settings reported by llama-bench |
+| `bench_model_type`, `bench_size_gib`, `bench_params_b` | string, float, float | llama_bench | What llama-bench says it loaded |
+| `gpu_name`, `build` | string | llama_bench | Generic GPU name parsed from the output (e.g. `NVIDIA GeForce RTX 4090`) and llama.cpp build commit |
+| `suspicious_flags` | list | all | Empty when the row passed every check |
+
+### `suspicious_flags`
+
+| Flag | Meaning |
+| --- | --- |
+| `unknown_model`, `unknown_repo`, `unknown_model_in_session` | Not in the catalogue at the time |
+| `repo_not_in_model` | The repo holds a different model |
+| `implausible_tps` | Faster than memory bandwidth allows for that model (with 3× headroom) |
+| `benchmark_model_mismatch` | llama-bench's parameter count doesn't match the chosen model |
+| `unparseable_benchmark`, `incomplete_benchmark`, `incomplete_feedback` | Missing numbers or the model |
+| `conflicting_feedback`, `implausible_hardware` | Contradictory or impossible values |
+
+Flagged rows are kept so filters stay transparent; the Space leaves them out of its statistics.
+
+## Layout and updates
+
+```
+data/incoming/YYYY/MM/DD/<instance>-<UTC time>-<suffix>.parquet   # written by the Space, at most hourly
+data/events/YYYY-MM.parquet                                       # one file per finished month
+```
+
+Early each month the previous month is compacted into `data/events/YYYY-MM.parquet` (re-sent session rows reduced to the newest per visit) and its incoming files are deleted in the same commit. The `events` config reads both locations.
+
+## Privacy
+
+Never collected: accounts, names, emails, IP addresses, cookies, user agents, device fingerprints, or individual clicks. Pasted `llama-bench` output is parsed on the server and discarded; only the numbers and settings above are stored, never file paths, CPU model strings or build machines. Hardware is stored as classes. Browsers sending Global Privacy Control or Do Not Track send nothing unless the user opts in.
+
+## Limitations
+
+- **Self-selected.** Visitors are not a random sample of local LLM users.
+- **Planned, not owned.** Hardware on `session` rows is what a visitor planned for, which may not be what they have. Benchmarks and reports are more reliable.
+- **Self-reported.** Benchmark output can be edited before pasting; implausible values are flagged, not proven.
+
+## License
+
+CC BY 4.0.