metadata
datasets:
- Tesslate/UIGEN-T2
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
license: apache-2.0
tags:
- tinyllama
- causal-lm
- merged-lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
merged_from:
- snaplora-adapted
---
# TinyLlama (Merged LoRA)
This repository contains a TinyLlama model with LoRA weights merged into the base.
- **Base model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
- **Adapter:** `snaplora-adapted`
- **Merge date:** 2025-09-14 23:12:26Z UTC
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "<this-repo-id>"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
prompt = "Write a haiku about tiny models."
inputs = tok(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))
```
## Notes
- The adapter was merged into the base weights using `peft.PeftModel.merge_and_unload()`.
- Files are saved with `safetensors` when possible.