Views
No views yet
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen3-4B-Instruct-2507 |
| Hardware | NVIDIA A100 SXM4 40GB |
| Precision | bfloat16 |
| Max context length | 3072 tokens |
| Epochs | 2 |
| Learning rate | 3e-06 |
| Batch size (effective) | 8 |
| LoRA Rank / Alpha | r=64 / a=128 |
| Target Modules | All Linear Layers (Q,K,V,O,Gate,Up,Down) |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3import torch
4
5base = "Qwen/Qwen3-4B-Instruct-2507"
6adapter = "your_id/your-repo-name"
7
8tokenizer = AutoTokenizer.from_pretrained(base)
9model = AutoModelForCausalLM.from_pretrained(
10 base,
11 torch_dtype=torch.bfloat16,
12 device_map="auto",
13)
14model = PeftModel.from_pretrained(model, adapter)mark-22/alfworld_cleaned_for_agentbench_v4 - Focused on household task completion and navigation.mark-22/dbbench_cleaned_for_agentbench - Focused on SQL generation and database manipulation (UPDATE/SELECT).