Update README.md

README.md

@@ -1,3 +1,84 @@
 ---
 license: apache-2.0
+base_model: state-spaces/mamba-130m-hf
+tokenizer: yhavinga/dutch-llama-tokenizer
+datasets: Kalamazooter/GeminiPhiDutch
 ---
+# A Tiny Dutch model, just-about semi-coherent
+
+![RatelSlang](RatelSlang-Micro.jpg)
+
+## Overview
+
+An experimental fine-tune of [mamba-130m](https://hf.co/state-spaces/mamba-130m-hf) using the [GeminiPhi Dataset](https://hf.co/Kalamazooter/GeminiPhiDutch)
+
+# Usage
+
+You need to install `transformers` from `main` until `transformers=4.39.0` is released. 
+```bash
+pip install git+https://github.com/huggingface/transformers@main
+```
+
+We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: 
+
+```bash
+pip install causal-conv1d>=1.2.0
+pip install mamba-ssm
+```
+
+If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used.
+
+## Generation
+You can use the classic `generate` API:
+**setup (For Cuda)**
+```python
+from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
+import torch
+device = torch.device('cuda:0')
+tokenizer = AutoTokenizer.from_pretrained("Kalamazooter/RatelSlang-Micro-130M")
+model = MambaForCausalLM.from_pretrained("Kalamazooter/RatelSlang-Micro-130M")
+model = model.to(device)
+```
+**Inference**
+```python
+input_ids = tokenizer("**Vraag: Ik heb 4 schapen, per schaap heb ik 3 lammetjes, hoeveel lammetjes heb ik?\n\n Antwoord:", return_tensors="pt").input_ids.to(device)
+out = model.generate(input_ids, max_new_tokens=50)
+print(tokenizer.batch_decode(out))
+['<s> **Vraag: Ik heb 4 schapen, per schaap heb ik 3 lammetjes, hoeveel lammetjes heb ik?\n\n Antwoord:\n\n1. Bereken het aantal lammetjes dat je hebt: 4 schapen x 3 lammetjes per schaap = 12 lammetjes\n2. Bereken het aantal lammetjes dat je hebt: 12 lam']
+```
+
+## PEFT finetuning example
+In order to finetune using the `peft` library, it is recommend to keep the model in float32!
+
+```python
+from datasets import load_dataset
+from trl import SFTTrainer
+from peft import LoraConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
+tokenizer = AutoTokenizer.from_pretrained("Kalamazooter/RatelSlang-Micro-130M")
+model = AutoModelForCausalLM.from_pretrained("Kalamazooter/RatelSlang-Micro-130M")
+dataset = load_dataset("Abirate/english_quotes", split="train")
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    logging_dir='./logs',
+    logging_steps=10,
+    learning_rate=2e-3
+)
+lora_config =  LoraConfig(
+        r=8,
+        target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
+        task_type="CAUSAL_LM",
+        bias="none"
+)
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    args=training_args,
+    peft_config=lora_config,
+    train_dataset=dataset,
+    dataset_text_field="quote",
+)
+trainer.train()
+```