Instructions to use PINKlab/OSHE-Local-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use PINKlab/OSHE-Local-v2 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("PINKlab/OSHE-Local-v2") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use PINKlab/OSHE-Local-v2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "PINKlab/OSHE-Local-v2" --prompt "Once upon a time"
- Atomic Chat
|
Download EVALUATION.md from PINKlab/OSHE-Local-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/PINKlab/OSHE-Local-v2/resolve/main/EVALUATION.md
- Command line
-
hf download hf://PINKlab/OSHE-Local-v2/EVALUATION.md
-
curl -L -o EVALUATION.md https://huggingface.co/PINKlab/OSHE-Local-v2/resolve/main/EVALUATION.md
2.93 kB
| # OSHE Local v2 Evaluation Note | |
| ## Scope | |
| This release comparison measures one product route: multi-document analysis. | |
| It does not measure broad general intelligence, open-domain knowledge or parity | |
| with larger foundation models. | |
| The comparison uses the same eight multi-document records from a frozen | |
| 32-record product fixture, the same evaluation gates, deterministic decoding | |
| with temperature 0 and seed 42, and the same Gemma 4 E2B foundation checkpoint. | |
| The two evaluated configurations are: | |
| 1. the unmodified Gemma 4 E2B base route; and | |
| 2. the complete OSHE Local v2 route, including its signed learned capability | |
| pack and required source-grounding guard. | |
| ## Results | |
| | Measurement | Gemma 4 E2B base | OSHE Local v2 | Change | | |
| | --- | ---: | ---: | ---: | | |
| | Reference-score attainment | 65% | 95% | +30 percentage points | | |
| | Schema compliance | 87.5% | 100% | +12.5 percentage points | | |
| | Non-overconfidence | 87.5% | 100% | +12.5 percentage points | | |
| | Parse-error rate | 0% | 0% | no change | | |
| Reference-score attainment is a task-specific score-versus-reference ratio. It | |
| is not an academic benchmark score. Schema compliance measures whether the | |
| response satisfied the structured multi-document contract. Non-overconfidence | |
| measures whether the response avoided unsupported certainty when evidence was | |
| missing or conflicting. | |
| ## Reproducibility Bindings | |
| | Artifact | SHA-256 | | |
| | --- | --- | | |
| | Gemma 4 E2B foundation artifact | `2ecb35dcf661866c74f7d45719bcf6c71e71bcf96f900b1e98968f64838effe1` | | |
| | Frozen 32-record fixture | `011d99a0dacdb83425cf0068f5be8d41cb594f8b5f3b62289f7a6cf37b54ad2d` | | |
| | Evaluation gates | `bd7ea33455171aae5845c520ea0032ab9d76221546ca9916a82dfae3802bedad` | | |
| | Gemma base predictions | `eab7a8fed1378cba04fa91579e58d483471e7fe0d1b58d2ed8c5d70143b69379` | | |
| | OSHE route predictions | `319d43fe48fb25f19c4e7ef884da88d41846980f566ef7f3048762bb1f57298d` | | |
| | Public learned-pack weights | `c02e8c255b12a88f84570a7afa58b81f215e78b56069f2bb9a908415772ca857` | | |
| ## Limitations | |
| - The evaluated multi-document subset contains eight cases and is intended as | |
| a release gate, not a comprehensive research benchmark. | |
| - The historical aggregate evaluator reports `all_gates_passed: false` because | |
| its Cantonese-mimicry diagnostic scored `12.5%`. That diagnostic was | |
| superseded for this route: the released contract requires formal written | |
| Traditional Chinese, not colloquial Cantonese imitation. A subsequent frozen | |
| semantic review passed all `16/16` Traditional-Chinese outputs for formal | |
| register, while the original register-drift gate also remained at `0%`. | |
| - The result measures the complete routed product behaviour. It must not be | |
| presented as the raw capability of the learned pack in isolation. | |
| - The result does not establish performance on unrelated chat, coding, | |
| browsing, search or tool-use tasks. | |
| - No claim of equivalence to Qwen 27B or any other larger model is made. | |