---
base_model: haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B
language:
- en
library_name: transformers
license: mit
datasets:
- postgrammar/london-llm-1800
quantized_by: ncky
tags:
- text-generation-inference
- transformers
- llama
- gguf
- historical
---
## About

static and imatrix-assisted GGUF quants of https://huggingface.co/haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B.

Generated with `llama.cpp` build `8044` (`91ea5d67f`).

`IQ4_XS` was quantized with an imatrix generated on 19th-century public-domain English text.

Note: this model has FFN dimensions (`5504`) not divisible by `256`, so `llama.cpp` applied fallback quantization to 22 tensors for K/IQ quant types.

## Base Model Info (from original model card)

Source: https://huggingface.co/haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B

| Detail | Value |
| :--- | :--- |
| Model Architecture | LlamaForCausalLM (decoder-only transformer) |
| Parameter Count | ~1.22B |
| Training Type | Trained from scratch (random initialization) |
| Tokenizer | Custom BPE, vocab size 32,000 |
| Sequence Length | 2048 |
| Attention Type | Grouped Query Attention (16 Q heads / 8 KV heads) |
| Hidden Size | 2048 |
| Intermediate Size | 5504 |
| Layers | 22 |

Training details reported by the source model card:
- Final training loss: 3.3951
- Start training loss: 10.7932
- Training steps: 182,000
- Epochs: 0.4997
- Training time: 117h 51m
- Reported training cost: $340.97 on an H100 SXM (RunPod)

## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details.

## Provided Quants

(sorted by size, not necessarily quality)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q2_K.gguf) | Q2_K | 0.5 | smallest |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q3_K_S.gguf) | Q3_K_S | 0.6 | low VRAM |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q3_K_M.gguf) | Q3_K_M | 0.6 | balanced low size |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q3_K_L.gguf) | Q3_K_L | 0.6 | better than Q3_K_M |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.IQ4_XS.gguf) | IQ4_XS | 0.6 | imatrix, recommended at this size |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q4_K_M.gguf) | Q4_K_M | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q5_K_S.gguf) | Q5_K_S | 0.8 | higher quality |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q5_K_M.gguf) | Q5_K_M | 0.9 | higher quality |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q6_K.gguf) | Q6_K | 1.0 | very good quality |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |
| [GGUF](https://huggingface.co/ncky/TimeCapsuleLLM-v2-llama-1.2B-GGUF/resolve/main/TimeCapsuleLLM-v2-llama-1.2B.f16.gguf) | f16 | 2.3 | 16 bpw, overkill |