Text Classification
Transformers
Safetensors
MLX
English
qwen3
feature-extraction
tinyjev
jev
decision-model
system-one
typed-decisions
text-embeddings-inference
Instructions to use AnkitAI/TinyJev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnkitAI/TinyJev-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AnkitAI/TinyJev-4B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnkitAI/TinyJev-4B") model = AutoModel.from_pretrained("AnkitAI/TinyJev-4B", device_map="auto") - MLX
How to use AnkitAI/TinyJev-4B with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir TinyJev-4B AnkitAI/TinyJev-4B
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
| { | |
| "format": "tinyjev-v2", | |
| "family": "pointer", | |
| "name": "TinyJev-4B", | |
| "head": { | |
| "head_dim": 256, | |
| "temperature": 1.0, | |
| "option_isolation": false | |
| }, | |
| "tokenizer": { | |
| "eos_token_id": 151643, | |
| "pad_token_id": 151643 | |
| }, | |
| "max_state": 8192, | |
| "max_branch": 8192, | |
| "dtypes": { | |
| "backbone": "float16", | |
| "head": "float32" | |
| }, | |
| "upstream": { | |
| "repo": "AnkitAI/tinyjev-4b", | |
| "trained_with": "Kev study runner (jaredpalmer/kev @ 2855ba2), trial tinyjev-e11-4b: LoRA r16 lr 5e-5 seed 2, 2 epochs on decision-v7 train (12,576 records), one H100, 45 min; LoRA merged into the base here", | |
| "base_model": "Qwen/Qwen3-4B-Base", | |
| "base_revision": "906bfd4b4dc7f14ee4320094d8b41684abff8539", | |
| "lora_rank": 16, | |
| "lora_alpha": 32, | |
| "merged_tensors": 252, | |
| "full_finetune": false | |
| } | |
| } |