This is an initial smoke-trained adapter, not a production-grade instruction model. It is useful as a reproducible starting point for local Qwen3 fine-tuning, adapter loading, and follow-up GGUF conversion work.

Model Details

Base model: Qwen/Qwen3-0.6B
Adapter type: LoRA
Training method: supervised fine-tuning with TRL SFTTrainer
Dataset: trl-lib/Capybara
Hardware used for this run: Apple Silicon MPS
Frameworks: transformers, peft, trl, torch

Training Summary

This first uploaded version was trained as a local smoke run:

Metric	Value
Train samples	8
Eval samples	2
Max steps	1
Max length	256
Train loss	1.1917
Baseline eval loss	2.4017
Final eval loss	2.2526

The eval set is intentionally tiny, so these numbers only prove that the training pipeline ran end to end. They should not be interpreted as robust model quality benchmarks.

Usage

Install dependencies:

pip install transformers peft accelerate torch

Load the adapter:

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

base_model = "Qwen/Qwen3-0.6B"
adapter_id = "edgemindroboticslabs/qwen3-0.6b-capybara-sft"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [{"role": "user", "content": "Give me three practical tips for organizing a small robotics lab."}]
print(pipe(messages, max_new_tokens=160, do_sample=True, temperature=0.7)[0]["generated_text"])

Intended Use

This adapter is intended for:

Reproducible Qwen3 LoRA fine-tuning experiments
Local instruction-tuning tests
Adapter merge and quantization experiments
Educational examples for Hugging Face PEFT workflows

Limitations

This is a 1-step smoke-trained adapter and is not expected to outperform the base model.
The evaluation split is too small for reliable quality claims.
It may inherit limitations and biases from the base model and dataset.
It has not been safety-aligned beyond the behavior of the base model.

Next Planned Improvements

Run a longer SFT pass with more samples.
Publish merged full-model weights when appropriate.
Publish GGUF quantizations for llama.cpp, LM Studio, and Ollama-style local usage.
Add prompt examples and more meaningful evals.

Reproducibility

The local training script used for this adapter is:

uv run --python /opt/homebrew/bin/python3.11 scripts/local_train_sft.py

Key settings:

text

BASE_MODEL=Qwen/Qwen3-0.6B
DATASET_ID=trl-lib/Capybara
MAX_TRAIN_SAMPLES=8
MAX_EVAL_SAMPLES=2
MAX_STEPS=1
MAX_LENGTH=256