---
license: cc-by-nc-4.0
language:
- en
base_model: Qwen/Qwen3-1.7B-Base
datasets:
- MBZUAI/LaMini-instruction
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- instruction-tuning
- sft
- qlora
- lora
- merged
---

# qwen3-1.7b-lamini-qlora-instruction-tuned

Instruction-tuned **Qwen3-1.7B-Base** using **SFT (QLoRA/LoRA)** on **MBZUAI/LaMini-instruction**, then **merged** into a single model checkpoint for easy deployment (single-turn Q/A).


## Model Details

- **Base model**: `Qwen/Qwen3-1.7B-Base`
- **Finetuning**: Supervised Fine-Tuning (SFT) with QLoRA
- **Dataset**: `MBZUAI/LaMini-instruction` (we utilized half of the data)
- **Output**: LoRA merged into base weights and saved as standard HF causal LM weights.

### Prompt Format (Important)

This model was trained with the following text instruction format:

Instruction:

{instruction}

Input:

{input}

Response:

{model generates here}

If you omit `### Input:`, use:

Instruction:

{instruction}

Response:

{model generates here}

For convenience, this repository may include helper utilities such as `prompt_format.py`.

## Quickstart (Transformers)

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "ericoh929/qwen3-1.7b-lamini-qlora-instruction-tuned"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
)
model.eval()

instruction = "Answer the question concisely."
inp = "If Tom has 3 apples and buys 4 more, how many apples does he have?"

prompt = (
    f"### Instruction:\n{instruction}\n\n"
    f"### Input:\n{inp}\n\n"
    f"### Response:\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    out = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

# Decode only the generated continuation (recommended)
gen_ids = out[0][inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
print(answer)
```


Intended Use

	•	Single-turn instruction following
    
	•	General Q/A, short reasoning, summarization style tasks

Limitations

	•	Trained on a synthetic/large instruction dataset; outputs can contain hallucinations.
    
	•	Best results are achieved when using the training prompt format shown above.
    
	•	This is a 1.7B model; complex reasoning / long-context tasks may be limited.
    

Training Notes 

	•	Method: QLoRA (4-bit base during training) + LoRA adapters
    
	•	Merge: Loaded base model in fp16/bf16 and merged adapters with merge_and_unload()
    
	•	max_seq_len: 2048