Text Generation
PEFT
Safetensors
English
jumplander
jx
qwen2.5
qwen2.5-coder
coding-agent
agentic-ai
software-engineering
repository-understanding
goal-grounding
tool-use
behavioral-policy
qlora
lora
conversational
Instructions to use jumplander/JX-Coder-7B-Agent-Behavior with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jumplander/JX-Coder-7B-Agent-Behavior with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "jumplander/JX-Coder-7B-Agent-Behavior") - Notebooks
- Google Colab
- Kaggle
File size: 618 Bytes
ff28506 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"name": "JX Coder 7B Agent",
"developer": "JumpLander",
"base_model": "Qwen/Qwen2.5-Coder-7B-Instruct",
"training_method": "4-bit QLoRA",
"mode": "full",
"train_examples": 14265,
"eval_examples": 751,
"max_length": 1024,
"lora_r": 16,
"learning_rate": 0.0001,
"metrics": {
"train_runtime": 49003.1816,
"train_samples_per_second": 0.291,
"train_steps_per_second": 0.018,
"total_flos": 5.5325230506554266e+17,
"train_loss": 0.0687537745764738,
"epoch": 1.0
},
"dataset_files": [
"goal_grounding_sample.jsonl",
"identity_data.jsonl"
]
} |