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README.md

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+---
+base_model: mesolitica/malaysian-mistral-1.1B-4096
+inference: false
+language:
+- ms
+model_creator: mesolitica
+model_name: malaysian-mistral-1.1B-4096
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# mesolitica/malaysian-mistral-1.1B-4096-GGUF
+
+Quantized GGUF model files for [malaysian-mistral-1.1B-4096](https://huggingface.co/mesolitica/malaysian-mistral-1.1B-4096) from [mesolitica](https://huggingface.co/mesolitica)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [malaysian-mistral-1.1b-4096.fp16.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.fp16.gguf) | fp16 | 2.25 GB  |
+| [malaysian-mistral-1.1b-4096.q2_k.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q2_k.gguf) | q2_k | 491.42 MB  |
+| [malaysian-mistral-1.1b-4096.q3_k_m.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q3_k_m.gguf) | q3_k_m | 561.96 MB  |
+| [malaysian-mistral-1.1b-4096.q4_k_m.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q4_k_m.gguf) | q4_k_m | 682.68 MB  |
+| [malaysian-mistral-1.1b-4096.q5_k_m.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q5_k_m.gguf) | q5_k_m | 799.13 MB  |
+| [malaysian-mistral-1.1b-4096.q6_k.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q6_k.gguf) | q6_k | 922.87 MB  |
+| [malaysian-mistral-1.1b-4096.q8_0.gguf](https://huggingface.co/afrideva/malaysian-mistral-1.1B-4096-GGUF/resolve/main/malaysian-mistral-1.1b-4096.q8_0.gguf) | q8_0 | 1.19 GB  |
+
+
+
+## Original Model Card:
+# Pretrain 1.1B 4096 context length Mistral on Malaysian text
+
+README at https://github.com/mesolitica/malaya/tree/5.1/pretrained-model/mistral
+
+- Dataset gathered at https://github.com/malaysia-ai/dedup-text-dataset/tree/main/pretrain-llm
+- We use Ray cluster to train on 5 nodes of 4x A100 80GB, https://github.com/malaysia-ai/jupyter-gpu/tree/main/ray
+
+WandB, https://wandb.ai/mesolitica/pretrain-mistral-1.1b?workspace=user-husein-mesolitica
+
+WandB report, https://wandb.ai/mesolitica/pretrain-mistral-3b/reports/Pretrain-Larger-Malaysian-Mistral--Vmlldzo2MDkyOTgz
+
+## how-to
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+TORCH_DTYPE = 'bfloat16'
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
+)
+
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-mistral-1.1B-4096', model_input_names = ['input_ids'])
+model = AutoModelForCausalLM.from_pretrained(
+    'mesolitica/malaysian-mistral-1.1B-4096',
+    use_flash_attention_2 = True,
+    quantization_config = nf4_config
+)
+prompt = '<s>nama saya'
+inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+
+generate_kwargs = dict(
+    inputs,
+    max_new_tokens=512,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.9,
+    do_sample=True,
+    num_beams=1,
+    repetition_penalty=1.05,
+)
+r = model.generate(**generate_kwargs)
+```