README.md

metadata

base_model: BEE-spoke-data/verysmol_llama-v11-KIx2
datasets:
  - BEE-spoke-data/knowledge-inoc-concat-v1
inference: false
license: apache-2.0
metrics:
  - accuracy
model_creator: BEE-spoke-data
model_name: verysmol_llama-v11-KIx2
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - generated_from_trainer
  - gguf
  - ggml
  - quantized
  - q2_k
  - q3_k_m
  - q4_k_m
  - q5_k_m
  - q6_k
  - q8_0
widget:
  - example_title: El Microondas
    text: My name is El Microondas the Wise and
  - example_title: Kennesaw State University
    text: Kennesaw State University is a public
  - example_title: Bungie
    text: >-
      Bungie Studios is an American video game developer. They are most famous
      for developing the award winning Halo series of video games. They also
      made Destiny. The studio was founded
  - example_title: Mona Lisa
    text: The Mona Lisa is a world-renowned painting created by
  - example_title: Harry Potter Series
    text: >-
      The Harry Potter series, written by J.K. Rowling, begins with the book
      titled
  - example_title: Riddle
    text: >-
      Question: I have cities, but no houses. I have mountains, but no trees. I
      have water, but no fish. What am I?

      Answer:
  - example_title: Photosynthesis
    text: The process of photosynthesis involves the conversion of
  - example_title: Story Continuation
    text: >-
      Jane went to the store to buy some groceries. She picked up apples,
      oranges, and a loaf of bread. When she got home, she realized she forgot
  - example_title: Math Problem
    text: >-
      Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
      and another train leaves Station B at 10:00 AM and travels at 80 mph, when
      will they meet if the distance between the stations is 300 miles?

      To determine
  - example_title: Algorithm Definition
    text: In the context of computer programming, an algorithm is

BEE-spoke-data/verysmol_llama-v11-KIx2-GGUF

Quantized GGUF model files for verysmol_llama-v11-KIx2 from BEE-spoke-data

Name	Quant method	Size
verysmol_llama-v11-kix2.fp16.gguf	fp16	116.89 MB
verysmol_llama-v11-kix2.q2_k.gguf	q2_k	30.14 MB
verysmol_llama-v11-kix2.q3_k_m.gguf	q3_k_m	33.71 MB
verysmol_llama-v11-kix2.q4_k_m.gguf	q4_k_m	38.34 MB
verysmol_llama-v11-kix2.q5_k_m.gguf	q5_k_m	43.21 MB
verysmol_llama-v11-kix2.q6_k.gguf	q6_k	48.39 MB
verysmol_llama-v11-kix2.q8_0.gguf	q8_0	62.45 MB

Original Model Card:

verysmol_llama-v11-KIx2

Model description

This model is a fine-tuned version of v10 (refinedweb-3m dedup) further trained for 2 epochs on KI dataset.

It achieves the following results on the evaluation set:

• Loss: 2.8876
• Accuracy: 0.4502

---

evals

hf-causal-experimental (pretrained=pszemraj/verysmol_llama-v11-KIx2,revision=main,trust_remote_code=True,dtype='float'), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16

Task	Version	Metric	Value		Stderr
arc_easy	0	acc	0.4024	±	0.0101
		acc_norm	0.3788	±	0.0100
boolq	1	acc	0.6199	±	0.0085
lambada_openai	0	ppl	111.9939	±	4.6906
		acc	0.2354	±	0.0059
openbookqa	0	acc	0.1440	±	0.0157
		acc_norm	0.2760	±	0.0200
piqa	0	acc	0.5713	±	0.0115
		acc_norm	0.5664	±	0.0116
winogrande	0	acc	0.5201	±	0.0140

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	0.1971	±	0.0116
		acc_norm	0.2278	±	0.0123

Task	Version	Metric	Value		Stderr
hellaswag	0	acc	0.2618	±	0.0088
		acc_norm	0.2797	±	0.0090

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	0.2509	±	0.0152
		mc2	0.4492	±	0.0156

---

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.00014
• train_batch_size: 16
• eval_batch_size: 16
• seed: 17514
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06
• lr_scheduler_type: inverse_sqrt
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.0681	0.03	150	3.0689	0.4259
3.0113	0.07	300	3.0433	0.4278
2.9468	0.1	450	3.0362	0.4288
3.0162	0.13	600	3.0148	0.4326
2.9531	0.17	750	3.0012	0.4341
2.9282	0.2	900	2.9923	0.4358
2.9485	0.23	1050	2.9845	0.4357
2.9365	0.27	1200	2.9749	0.4375

...

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.8215	1.7	7650	2.8943	0.4496
2.7714	1.74	7800	2.8914	0.4501
2.8132	1.77	7950	2.8913	0.4500
2.8505	1.8	8100	2.8906	0.4502
2.8294	1.84	8250	2.8901	0.4502
2.7977	1.87	8400	2.8891	0.4499
2.7501	1.9	8550	2.8878	0.4505
2.8038	1.94	8700	2.8883	0.4504
2.7547	1.97	8850	2.8876	0.4502

---