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Model: ericoh929/qwen3-1.7b-lamini-qlora-instruction-tuned
Source: Original Platform

README.md

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+---
+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