| --- | |
| 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. | |