tiny-lm-chat
About this model
tiny-lm
This repository provides a tiny 16M parameters language model for debugging and testing purposes. This is created by tuning sbintuitions/tiny-lm with oasset1 datasets in Japanese and English.
How to use
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm-chat", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm-chat", use_fast=False)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello!"}], add_generation_prompt=True, tokenize=False)
print(generator(prompt, max_length=30, do_sample=True, top_k=100))
Model architecture
A 4-layer, 512-hidden-size transformer-based language model.
Training
The model was first pre-trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens. And then it was fine-tuned on oasst1 datasets in Japanese and English for 15 epochs.
License
Technical Specs
- Parameters: 0.016B
- Architecture: Transformer
- Context Window: 2,048 tokens
- Input Modalities: text
Hardware Requirements
- VRAM: 0.5 GB
- Compute: CPU or any GPU with 512MB+ VRAM
Related Models
More Open Source →
Description: The NVIDIA DeepSeek-V4-Pro-NVFP4 model is the quantized version of the DeepSeek-V4-Pro model, which is a Mixture-of-Experts (MoE) language model with 1.6 trillion total parameters and 49 billion activated parameters. For more information, please check here. The NVIDI…
Description: The NVIDIA GLM-5.2 NVFP4 model is the quantized version of ZAI’s GLM-5.2 model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.2 is a Mixture-of-Experts (MoE) model for reasoning and coding that uses sparse attention…
👋 Join our WeChat or Discord community. 📖 Check out the GLM-5.2 blog and GLM-5 Technical report . 📍 Use GLM-5.2 API services on Z.ai API Platform. 🔜 Try GLM-5.2 here .
👋 Join our WeChat or Discord community. 📖 Check out the GLM-5.2 blog and GLM-5 Technical report . 📍 Use GLM-5.2 API services on Z.ai API Platform. 🔜 Try GLM-5.2 here .