base_model, language, library_name, license, datasets, quantized_by, tags

base_model	language	library_name	license	datasets	quantized_by	tags
haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B	en	transformers	mit	postgrammar/london-llm-1800	ncky	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 for more details.

Provided Quants

(sorted by size, not necessarily quality)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.5	smallest
GGUF	Q3_K_S	0.6	low VRAM
GGUF	Q3_K_M	0.6	balanced low size
GGUF	Q3_K_L	0.6	better than Q3_K_M
GGUF	IQ4_XS	0.6	imatrix, recommended at this size
GGUF	Q4_K_S	0.7	fast, recommended
GGUF	Q4_K_M	0.7	fast, recommended
GGUF	Q5_K_S	0.8	higher quality
GGUF	Q5_K_M	0.9	higher quality
GGUF	Q6_K	1.0	very good quality
GGUF	Q8_0	1.2	fast, best quality
GGUF	f16	2.3	16 bpw, overkill