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
File size: 825 Bytes
29c6fb1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"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
}
} |