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README.md
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pipeline_tag: question-answering
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tags:
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- art
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-
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pipeline_tag: question-answering
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tags:
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- art
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---
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# tinygoop-1
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## Model Description
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A fine-tuned version of TinyLlama-1.1B-Chat with room temp iq -> quantized to 4 bits and trained on copypastas
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## Intended Use
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- **Primary Use:** Not much, it barely can hold a conversation
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- **Secondary Uses:** brainrot generation, funny responses
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- **Out-of-scope:** Professional/business applications, factual question answering, safety-critical applications
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---
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## Training Data
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**Sources:**
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- 334,165 copypastas
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- The script from the television show "House"
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### Hardware used in training
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- **GPU:** NVIDIA GeForce RTX 4090
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- **CUDA:** 12.1
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- **Framework:** PyTorch 2.5.1+cu121
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- **Transformers:** Latest
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- **PEFT:** Latest
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- **BitsAndBytes:** 4-bit quantization
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---
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### Basic Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "S-teven/tinygoop-1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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prompt = "hey"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=1.2,
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top_p=0.95,
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repetition_penalty=1.05
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Hardware Requirements
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| Precision | VRAM Required | Hardware |
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|-----------|---------------|----------|
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| 4-bit Quantized | ~800MB | Any modern GPU |
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| CPU (FP32) | ~4GB RAM | Modern CPU (slow) |
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---
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## Limitations & Biases
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**Content Warning:** This model was trained on copypasta data and may generate:
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- Offensive or inappropriate content
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- Nonsensical or chaotic responses
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- Biases present in online communities
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**Not suitable for:**
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- Most things
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- Professional or business use
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- Educational applications
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- Factual information retrieval
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- Content requiring safety guarantees
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