README.md

---
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.