File size: 2,043 Bytes
ca0c42c 2fb5535 cd6c462 2fb5535 cd6c462 2fb5535 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
license: mit
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: question-answering
tags:
- art
---
# tinygoop-1.1b
## Model Description
A fine-tuned version of TinyLlama-1.1B-Chat with room temp iq -> quantized to 4 bits and trained on copypastas
## Intended Use
- **Primary Use:** Not much, it barely can hold a conversation
- **Secondary Uses:** brainrot generation, funny responses
- **Out-of-scope:** Professional/business applications, factual question answering, safety-critical applications
---
## Training Data
**Sources:**
- 334,165 copypastas
- The script from the television show "House"
### Hardware used in training
- **GPU:** NVIDIA GeForce RTX 4090
- **CUDA:** 12.1
- **Framework:** PyTorch 2.5.1+cu121
- **Transformers:** Latest
- **PEFT:** Latest
- **BitsAndBytes:** 4-bit quantization
---
### Basic Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "S-teven/tinygoop-1.1b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
prompt = "hey"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=1.2,
top_p=0.95,
repetition_penalty=1.05
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Hardware Requirements
| Precision | VRAM Required | Hardware |
|-----------|---------------|----------|
| 4-bit Quantized | ~800MB | Any modern GPU |
| CPU (FP32) | ~4GB RAM | Modern CPU (slow) |
---
## Limitations & Biases
**Content Warning:** This model was trained on copypasta data and may generate:
- Offensive or inappropriate content
- Nonsensical or chaotic responses
- Biases present in online communities
**Not suitable for:**
- Most things
- Professional or business use
- Educational applications
- Factual information retrieval
- Content requiring safety guarantees
|