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base_model: openai/gpt-oss-20b
library_name: peft
license: apache-2.0
tags:
- lora
- peft
- safetensors
- transformers
- question-answering
- conversational
- text-generation
language:
- en
---
# SpaceLLM Single Turn QA — LoRA Adapter for Single-Turn Instruction Following
SpaceLLM Single Turn QA is a parameter-efficient LoRA adapter fine-tuned on top of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) for single-turn question answering and instruction following. Only the attention projection layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`) are trained; the full transformer backbone remains frozen, keeping the adapter extremely lightweight while steering the model's outputs toward accurate, focused single-turn responses.
---
## Model Details
### Model Description
- **Developed by:** AdityaPS
- **Model type:** LoRA adapter (PEFT) over a causal language model
- **Base model:** [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) (22B params, BF16/MXFP4)
- **Language(s):** English
- **License:** Apache 2.0
- **Task:** Causal LM / single-turn question answering, instruction following
### Adapter Configuration
| Parameter | Value |
|---|---|
| PEFT type | LoRA |
| Rank (`r`) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
| Bias | none |
| Task type | CAUSAL_LM |
| PEFT version | 0.19.1 |
---
## How to Get Started
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "openai/gpt-oss-20b"
adapter_id = "AdityaPS/SpaceLLM_Single_Turn_QA"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)
prompt = "Your question here"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## Training Details
This adapter was trained using LoRA on the attention projection layers only, keeping the base model frozen. This makes the adapter lightweight to store and share while adapting the model's behavior for single-turn Q&A and instruction-following tasks.
---
## Framework Versions
- PEFT 0.19.1 |