Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) lmlab-mistral-1b-untrained - GGUF - Model creator: https://huggingface.co/lmlab/ - Original model: https://huggingface.co/lmlab/lmlab-mistral-1b-untrained/ | Name | Quant method | Size | | ---- | ---- | ---- | | [lmlab-mistral-1b-untrained.Q2_K.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q2_K.gguf) | Q2_K | 0.44GB | | [lmlab-mistral-1b-untrained.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.IQ3_XS.gguf) | IQ3_XS | 0.49GB | | [lmlab-mistral-1b-untrained.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.IQ3_S.gguf) | IQ3_S | 0.5GB | | [lmlab-mistral-1b-untrained.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q3_K_S.gguf) | Q3_K_S | 0.5GB | | [lmlab-mistral-1b-untrained.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.IQ3_M.gguf) | IQ3_M | 0.51GB | | [lmlab-mistral-1b-untrained.Q3_K.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q3_K.gguf) | Q3_K | 0.54GB | | [lmlab-mistral-1b-untrained.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q3_K_M.gguf) | Q3_K_M | 0.54GB | | [lmlab-mistral-1b-untrained.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q3_K_L.gguf) | Q3_K_L | 0.58GB | | [lmlab-mistral-1b-untrained.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.IQ4_XS.gguf) | IQ4_XS | 0.6GB | | [lmlab-mistral-1b-untrained.Q4_0.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q4_0.gguf) | Q4_0 | 0.63GB | | [lmlab-mistral-1b-untrained.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.IQ4_NL.gguf) | IQ4_NL | 0.63GB | | [lmlab-mistral-1b-untrained.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q4_K_S.gguf) | Q4_K_S | 0.63GB | | [lmlab-mistral-1b-untrained.Q4_K.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q4_K.gguf) | Q4_K | 0.66GB | | [lmlab-mistral-1b-untrained.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q4_K_M.gguf) | Q4_K_M | 0.66GB | | [lmlab-mistral-1b-untrained.Q4_1.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q4_1.gguf) | Q4_1 | 0.69GB | | [lmlab-mistral-1b-untrained.Q5_0.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q5_0.gguf) | Q5_0 | 0.74GB | | [lmlab-mistral-1b-untrained.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q5_K_S.gguf) | Q5_K_S | 0.74GB | | [lmlab-mistral-1b-untrained.Q5_K.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q5_K.gguf) | Q5_K | 0.76GB | | [lmlab-mistral-1b-untrained.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q5_K_M.gguf) | Q5_K_M | 0.76GB | | [lmlab-mistral-1b-untrained.Q5_1.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q5_1.gguf) | Q5_1 | 0.8GB | | [lmlab-mistral-1b-untrained.Q6_K.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q6_K.gguf) | Q6_K | 0.87GB | | [lmlab-mistral-1b-untrained.Q8_0.gguf](https://huggingface.co/RichardErkhov/lmlab_-_lmlab-mistral-1b-untrained-gguf/blob/main/lmlab-mistral-1b-untrained.Q8_0.gguf) | Q8_0 | 1.12GB | Original model description: --- license: apache-2.0 language: - en pipeline_tag: text-generation --- Sorry everyone this got sort of popular but it doesnt generate understandable text - I think there's a way to make this generate good results w/ relatively little compute I'll experiment a bit later # LMLab Mistral 1B Untrained This is an untrained base model modified from Mistral-7B-Instruct. It has 1.13 billion parameters. ## Untrained This model is untrained. **This means it will not generate comprehensible text.** ## Model Details ### Model Description - **Developed by:** LMLab - **License:** Apache 2.0 - **Parameters:** 1.13 billion (1,134,596,096) - **Modified from model:** [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Architecture LMLab Mistral 1B is a transformer model, with the following architecture choices: * Grouped-Query Attention * Sliding-Window Attention * Byte-fallback BPE tokenizer ## Usage Use `MistralForCausalLM`. ```python from transformers import MistralForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('lmlab/lmlab-mistral-1b-untrained') model = MistralForCausalLM.from_pretrained('lmlab/lmlab-mistral-1b-untrained') text = "Once upon a time" encoded_input = tokenizer(text, return_tensors='pt') output = model.generate(**encoded_input) print(tokenizer.decode(output[0])) ``` ## Notice This model does not have any moderation systems.