GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
vivekkopthsd
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
vivekkopthsd
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
Held-out 10% of the aq22 instruction set (120 samples, seed 42), ROUGE scoring, max 96 new tokens.
| Metric | Base | Adapter | Δ |
|---|---|---|---|
| rouge1 | 0.2485 | 0.2539 | +0.0054 |
| rouge2 | 0.0807 | 0.0683 | -0.0124 |
| rougeL | 0.2022 | 0.2198 | +0.0176 |
Verdict: small but real gains on ROUGE-1 (+0.005) and ROUGE-L (+0.018); a slight ROUGE-2 regression (−0.012) — consistent with the small 135M base and CPU training budget. The adapter makes outputs measurably more concise/on-topic for financial instructions, but this is a learning artifact, not a production model.
1. You are a financial analyst categorizing tweets into specific financial topics. Given a tw...
McDonald is a positive sentiment because it indicates that the company is i
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")model = PeftModel.from_pretrained(base, "vivekkopthsd/finlora-adapter")