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| Parameter | Value |
|---|---|
| Rank (r) | 32 |
| Alpha | 64 |
| Dropout | 0.1 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Parameter | Value |
|---|---|
| Epochs | 2 |
| Batch Size | 8 |
| Learning Rate | 0.0001 |
| Max Sequence Length | 1024 |
1from peft import PeftModel
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4# Load base model
5base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it")
6tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
7
8# Load LoRA adapter
9model = PeftModel.from_pretrained(base_model, "Pista1981/hivemind-instruct-587c9d19")
10
11# Generate
12inputs = tokenizer("Your prompt here", return_tensors="pt")
13outputs = model.generate(**inputs, max_new_tokens=100)
14print(tokenizer.decode(outputs[0]))1# Merge adapter with base model
2merged_model = model.merge_and_unload()
3merged_model.save_pretrained("./merged-model")