Text Generation
MLX
Safetensors
Polish
English
llama
typed-decisions
decision-making
classification
calibration
polish
decision-model
conversational
8-bit precision
Instructions to use Remek/basal-2.0-max-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Remek/basal-2.0-max-MLX-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Remek/basal-2.0-max-MLX-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Remek/basal-2.0-max-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Remek/basal-2.0-max-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Remek/basal-2.0-max-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Remek/basal-2.0-max-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Download CALIBRATION.json from Remek/basal-2.0-max-MLX-8bit: direct link, hf CLI and curl.
- Browser
- Download file 3.74 kB
-
https://huggingface.co/Remek/basal-2.0-max-MLX-8bit/resolve/main/CALIBRATION.json
- Command line
-
hf download hf://Remek/basal-2.0-max-MLX-8bit/CALIBRATION.json
-
curl -L -o CALIBRATION.json https://huggingface.co/Remek/basal-2.0-max-MLX-8bit/resolve/main/CALIBRATION.json
3.74 kB
| { | |
| "prompt": 2, | |
| "orders": 1, | |
| "method": "temperature per question type and per option-count bucket (choice), by NLL, fitted on the out-of-distribution calibration split only; thresholds per type: the most permissive threshold whose accepted held-out calibration decisions (10 family folds, each refitted without its families) have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers", | |
| "temperature_per_prim": { | |
| "choice": 1.3542555469368927, | |
| "noul": 2.4130090616010436, | |
| "score": 2.3107053937445454 | |
| }, | |
| "temperature_per_bucket": { | |
| "choice": { | |
| "2-10": 1.6339154532410998, | |
| "11-26": 1.2782470235604306, | |
| "27-255": 0.9038571383911009 | |
| } | |
| }, | |
| "thresholds": { | |
| "0.01": { | |
| "confidence": null, | |
| "method": "per type: the most permissive threshold whose accepted held-out calibration decisions (10 family folds, each refitted without its families) have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers", | |
| "per_type": { | |
| "choice": { | |
| "confidence": null, | |
| "heldout_coverage": 0.0, | |
| "heldout_error": null | |
| }, | |
| "noul": { | |
| "confidence": null, | |
| "heldout_coverage": 0.0, | |
| "heldout_error": null | |
| }, | |
| "score": { | |
| "confidence": null, | |
| "heldout_coverage": 0.0, | |
| "heldout_error": null | |
| } | |
| }, | |
| "heldout_coverage": 0.0, | |
| "heldout_error": null | |
| }, | |
| "0.05": { | |
| "confidence": null, | |
| "method": "per type: the most permissive threshold whose accepted held-out calibration decisions (10 family folds, each refitted without its families) have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers", | |
| "per_type": { | |
| "choice": { | |
| "confidence": 0.9343460250507047, | |
| "heldout_coverage": 0.5076638032676436, | |
| "heldout_error": 0.029694757796947524 | |
| }, | |
| "noul": { | |
| "confidence": 0.8903203843605327, | |
| "heldout_coverage": 0.4565752128666036, | |
| "heldout_error": 0.013675922088686288 | |
| }, | |
| "score": { | |
| "confidence": null, | |
| "heldout_coverage": 0.0, | |
| "heldout_error": null | |
| } | |
| }, | |
| "heldout_coverage": 0.4191786264090977, | |
| "heldout_error": 0.02511550764127468 | |
| } | |
| }, | |
| "gates": { | |
| "ece_lofo": { | |
| "choice": { | |
| "pooled": 0.02410796982582807, | |
| "n": 11874, | |
| "gate": 0.05, | |
| "pass": true | |
| }, | |
| "noul": { | |
| "pooled": 0.04203932516042977, | |
| "n": 5285, | |
| "gate": 0.05, | |
| "pass": true | |
| }, | |
| "score": { | |
| "pooled": 0.03959409092958942, | |
| "n": 2978, | |
| "gate": 0.05, | |
| "pass": true | |
| } | |
| }, | |
| "ece_worst_fold": 0.0984699552179725, | |
| "worst_fold": "agent", | |
| "automated_at": { | |
| "0.01": 0.0, | |
| "0.05": 0.4191786264090977 | |
| }, | |
| "automated_error": { | |
| "0.01": null, | |
| "0.05": 0.02511550764127468 | |
| }, | |
| "note": "held-out numbers: every evaluation row predicted by the fit without its family" | |
| }, | |
| "test": { | |
| "T": { | |
| "choice": { | |
| "acc": 0.8283644938521139, | |
| "ece": 0.022511433054648682, | |
| "n": 11874 | |
| }, | |
| "noul": { | |
| "acc": 0.868306527909177, | |
| "ece": 0.04224814168670232, | |
| "n": 5285 | |
| }, | |
| "score": { | |
| "acc": 0.6638683680322364, | |
| "ece": 0.039465735336162315, | |
| "n": 2978 | |
| }, | |
| "all": { | |
| "acc": 0.8145205343397726, | |
| "ece": 0.02335889271209342, | |
| "n": 20137 | |
| } | |
| }, | |
| "note": "evaluation pool (development and test splits), full fit" | |
| }, | |
| "n": { | |
| "fit": 3560, | |
| "many": 1500, | |
| "eval": 20137 | |
| }, | |
| "engine": "fast", | |
| "fitted_on": { | |
| "split": "out-of-distribution calibration split (sealed-seed BH-, JDI- and agent-shaped families, many-option items)", | |
| "orders": 1, | |
| "readouts": "basal-serve --mode fast, bf16" | |
| }, | |
| "certified_on": "the same split (held-out folds)", | |
| "evaluated_on": "development and test splits, leave-one-family-out and leave-one-language-out" | |
| } | |