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cagataydev
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Model Details
Model Provider
cagataydev
Model Tree
Base
Qwen/Qwen3-0.6B
Adapter
this model
Input Modalities
Text
Output Modalities
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Supported Functionality
Dedicated Endpoints
Container
| metric | before | after |
|---|---|---|
| holdout NLL (strands corpus) | 2.172 | 1.581 (Δ −0.59, −27%) |
Training: 600 steps, LoRA r=32 (α=64, dropout 0.05) on q/k/v/o + gate/up/down, packed 1024-token blocks, bs 4 × accum 2, cosine LR 1e-4, bf16, single L40S.
python
from slm import SLMfrom strands import Agentmodel = SLM("cagataydev/strands-qwen3-0.6b") # base auto-resolved + adapter mergedagent = Agent(model=model)agent("How do I create a custom tool in Strands?") # learns from every turnmodel.save("experience.pt") # persist what it learned
Or plain transformers/peft:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", dtype="bfloat16")model = PeftModel.from_pretrained(base, "cagataydev/strands-qwen3-0.6b").merge_and_unload()tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
| repo | base | params | holdout NLL Δ |
|---|---|---|---|
| cagataydev/strands-qwen3-vl-2b | Qwen3-VL-2B-Instruct | 2B | 1.85 → ~1.0 |
| cagataydev/strands-gemma4-e2b | Gemma 4 E2B (QAT mobile) | 2B eff. | 2.69 → 1.26 |
| cagataydev/strands-qwen3-0.6b | Qwen3-0.6B | 0.6B | 2.17 → 1.58 |