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naazimsnh02
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Model Details
Model Provider
naazimsnh02
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
google/gemma-4-E4B-it (Gemma 4, ~4B params)| Parameter | Value |
|---|---|
| Base model | unsloth/gemma-4-E4B-it (4-bit quantized) |
| LoRA rank (r) | 32 |
| LoRA alpha | 64 |
| LoRA dropout | 0 |
| Target modules | attention + MLP (all language layers) |
| Max sequence length | 4096 |
| Effective batch size | 16 (micro-batch 1 × grad accum 16) |
| Epochs | 2 |
| Total steps | 3,738 |
| Learning rate | 1e-4 |
| LR scheduler | cosine (3% warmup) |
| Optimizer | AdamW 8-bit |
| Precision | bf16 |
| Weight decay | 0.01 |
| Training runtime | 59,813s (~16.6 hours) |
| Peak VRAM | 16.4 GB |
| Loss (start → end) | 0.587 → 0.085 |
~30,000 instruction-response pairs built from two open-source finance datasets:
The corpus is diversity-sampled (per-source and per-task-type caps) and 10-gram decontaminated against FLARE evaluation inputs (FPB, FiQA_SA, Headline) to prevent benchmark leakage.
FLARE-style multiple-choice accuracy on AdaptLLM/finance-tasks (greedy decoding, temp=0):
| Task | n | Accuracy |
|---|---|---|
| FPB | 970 | 78.4% |
| FiQA_SA | 235 | 67.2% |
| Headline | 20,547 | 69.2% |
| Macro avg | — | 71.6% |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained( "google/gemma-4-E4B-it", device_map="auto", torch_dtype="auto",)model = PeftModel.from_pretrained(base_model, "naazimsnh02/FinanceGemma-E4B-lora")tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/FinanceGemma-E4B-lora") prompt = "Classify the sentiment of this financial news: 'Apple reported record Q4 earnings, beating analyst estimates by 15%.'"messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)output = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(output[0], skip_special_tokens=True))python
from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( "naazimsnh02/FinanceGemma-E4B-lora", max_seq_length=4096, load_in_4bit=True,)FastModel.for_inference(model)Financial text analysis tasks including:
Apache 2.0 (same as the base Gemma 4 model)
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained( "google/gemma-4-E4B-it", device_map="auto", torch_dtype="auto",)model = PeftModel.from_pretrained(base_model, "naazimsnh02/FinanceGemma-E4B-lora")tokenizer = AutoTokenizer.from_pretrained("naazimsnh02/FinanceGemma-E4B-lora") prompt = "Classify the sentiment of this financial news: 'Apple reported record Q4 earnings, beating analyst estimates by 15%.'"messages = [{"role": "user", "content": [{"type": "text", "text": prompt}]}]inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)output = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(output[0], skip_special_tokens=True))from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( "naazimsnh02/FinanceGemma-E4B-lora", max_seq_length=4096, load_in_4bit=True,)FastModel.for_inference(model)