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