Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
decider
systemone
jev
decision-model
qwen3.5
post-train
text-to-typed-output
structured-output
classification
tool-calling
routing
moderation
custom-code
Eval Results (legacy)
Instructions to use Jeetu456/decider-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeetu456/decider-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeetu456/decider-2b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jeetu456/decider-2b") model = AutoModelForCausalLM.from_pretrained("Jeetu456/decider-2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 919 Bytes
cd02163 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import json, shutil, sys, os, torch
from transformers import AutoConfig, AutoModelForCausalLM
src, out = sys.argv[1], sys.argv[2]
full = json.load(open(f"{src}/config.json"))
text = dict(full["text_config"])
text.update(hidden_size=64, intermediate_size=128, num_hidden_layers=4, num_attention_heads=2, num_key_value_heads=1,
head_dim=32, linear_key_head_dim=16, linear_value_head_dim=16, linear_num_key_heads=2, linear_num_value_heads=2,
layer_types=["linear_attention"] * 3 + ["full_attention"], mtp_num_hidden_layers=0)
text["model_type"] = "qwen3_5_text"
cfg = AutoConfig.for_model(**text)
torch.manual_seed(0)
model = AutoModelForCausalLM.from_config(cfg)
print(type(model).__name__, sum(p.numel() for p in model.parameters()) / 1e6, "M params")
model.save_pretrained(out)
for f in ["tokenizer.json", "tokenizer_config.json", "vocab.json", "merges.txt"]:
shutil.copy(f"{src}/{f}", out)
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