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RichardErkhov/
raidhon_-_coven_tiny_1.1b_32k_orpo_alpha-gguf

Name: raidhon_-_coven_tiny_1.1b_32k_orpo_alpha-gguf
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coven_tiny_1.1b_32k_orpo_alpha - GGUF

Model creator: https://huggingface.co/raidhon/
Original model: https://huggingface.co/raidhon/coven_tiny_1.1b_32k_orpo_alpha/

Name	Quant method	Size
coven_tiny_1.1b_32k_orpo_alpha.Q2_K.gguf	Q2_K	0.4GB
coven_tiny_1.1b_32k_orpo_alpha.IQ3_XS.gguf	IQ3_XS	0.44GB
coven_tiny_1.1b_32k_orpo_alpha.IQ3_S.gguf	IQ3_S	0.47GB
coven_tiny_1.1b_32k_orpo_alpha.Q3_K_S.gguf	Q3_K_S	0.47GB
coven_tiny_1.1b_32k_orpo_alpha.IQ3_M.gguf	IQ3_M	0.48GB
coven_tiny_1.1b_32k_orpo_alpha.Q3_K.gguf	Q3_K	0.51GB
coven_tiny_1.1b_32k_orpo_alpha.Q3_K_M.gguf	Q3_K_M	0.51GB
coven_tiny_1.1b_32k_orpo_alpha.Q3_K_L.gguf	Q3_K_L	0.55GB
coven_tiny_1.1b_32k_orpo_alpha.IQ4_XS.gguf	IQ4_XS	0.57GB
coven_tiny_1.1b_32k_orpo_alpha.Q4_0.gguf	Q4_0	0.59GB
coven_tiny_1.1b_32k_orpo_alpha.IQ4_NL.gguf	IQ4_NL	0.6GB
coven_tiny_1.1b_32k_orpo_alpha.Q4_K_S.gguf	Q4_K_S	0.6GB
coven_tiny_1.1b_32k_orpo_alpha.Q4_K.gguf	Q4_K	0.62GB
coven_tiny_1.1b_32k_orpo_alpha.Q4_K_M.gguf	Q4_K_M	0.62GB
coven_tiny_1.1b_32k_orpo_alpha.Q4_1.gguf	Q4_1	0.65GB
coven_tiny_1.1b_32k_orpo_alpha.Q5_0.gguf	Q5_0	0.71GB
coven_tiny_1.1b_32k_orpo_alpha.Q5_K_S.gguf	Q5_K_S	0.71GB
coven_tiny_1.1b_32k_orpo_alpha.Q5_K.gguf	Q5_K	0.73GB
coven_tiny_1.1b_32k_orpo_alpha.Q5_K_M.gguf	Q5_K_M	0.73GB
coven_tiny_1.1b_32k_orpo_alpha.Q5_1.gguf	Q5_1	0.77GB
coven_tiny_1.1b_32k_orpo_alpha.Q6_K.gguf	Q6_K	0.84GB
coven_tiny_1.1b_32k_orpo_alpha.Q8_0.gguf	Q8_0	1.09GB

Original model description:

language:

en license: apache-2.0 tags:
text-generation
large-language-model
orpo dataset:
jondurbin/truthy-dpo-v0.1
AlekseyKorshuk/evol-codealpaca-v1-dpo
argilla/distilabel-intel-orca-dpo-pairs
argilla/ultrafeedback-binarized-avg-rating-for-dpo-filtered
snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
mlabonne/orpo-dpo-mix-40k

base_model:

TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index:
name: Coven Tiny 1.1B description: "Coven Tiny 1.1B is a derivative of TinyLlama 1.1B Chat, fine-tuned to perform specialized tasks involving deeper understanding and reasoning over context. This model exhibits strong capabilities in both general language understanding and task-specific challenges." results:
- task: type: text-generation name: Winogrande Challenge dataset: name: Winogrande type: winogrande config: winogrande_xl split: test args: num_few_shot: 5 metrics:
  - type: accuracy value: 61.17 name: accuracy
- task: type: text-generation name: TruthfulQA Generation dataset: name: TruthfulQA type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: accuracy value: 34.31 name: accuracy
- task: type: text-generation name: PIQA Problem Solving dataset: name: PIQA type: piqa split: validation args: num_few_shot: 5 metrics:
  - type: accuracy value: 71.06 name: accuracy
- task: type: text-generation name: OpenBookQA Facts dataset: name: OpenBookQA type: openbookqa split: test args: num_few_shot: 5 metrics:
  - type: accuracy value: 30.60 name: accuracy
- task: type: text-generation name: MMLU Knowledge Test dataset: name: MMLU type: mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: accuracy value: 38.03 name: accuracy
- task: type: text-generation name: Hellaswag Contextual Completions dataset: name: Hellaswag type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: accuracy value: 43.44 name: accuracy
- task: type: text-generation name: GSM8k Mathematical Reasoning dataset: name: GSM8k type: gsm8k split: test args: num_few_shot: 5 metrics:
  - type: accuracy value: 14.71 name: exact match (strict)
  - type: accuracy value: 14.63 name: exact match (flexible)
- task: type: text-generation name: BoolQ Question Answering dataset: name: BoolQ type: boolq split: validation args: num_few_shot: 5 metrics:
  - type: accuracy value: 65.20 name: accuracy
- task: type: text-generation name: ARC Challenge dataset: name: ARC Challenge type: ai2_arc split: test args: num_few_shot: 25 metrics:
  - type: accuracy value: 34.81 name: accuracy

🤏 Coven Tiny 1.1B 32K ORPO

Coven Tiny 1.1B 32K is an improved iteration of TinyLlama-1.1B-Chat-v1.0, refined to expand processing capabilities and refine language model preferences. This model includes a significantly increased context limit of 32K tokens, allowing for more extensive data processing and understanding of complex language scenarios. In addition, Coven Tiny 1.1B 32K uses the innovative ORPO (Monolithic Preference Optimization without Reference Model) technique. ORPO simplifies the fine-tuning process by directly optimizing the odds ratio to distinguish between favorable and unfavorable generation styles, effectively improving model performance without the need for an additional preference alignment step.

Model Details

Model name: Coven Tiny 1.1B 32K ORPO alpha
Fine-tuned by: raidhon
Base model: TinyLlama-1.1B-Chat-v1.0
Parameters: 1.1B
Context: 32K
Language(s): Multilingual
License: Apache2.0

Eval

Task	Model	Metric	Value	Change (%)
Winogrande	TinyLlama 1.1B Chat	Accuracy	61.56%	-
	Coven Tiny 1.1B	Accuracy	61.17%	-0.63%
TruthfulQA	TinyLlama 1.1B Chat	Accuracy	30.43%	-
	Coven Tiny 1.1B	Accuracy	34.31%	+12.75%
PIQA	TinyLlama 1.1B Chat	Accuracy	74.10%	-
	Coven Tiny 1.1B	Accuracy	71.06%	-4.10%
OpenBookQA	TinyLlama 1.1B Chat	Accuracy	27.40%	-
	Coven Tiny 1.1B	Accuracy	30.60%	+11.68%
MMLU	TinyLlama 1.1B Chat	Accuracy	24.31%	-
	Coven Tiny 1.1B	Accuracy	38.03%	+56.44%
Hellaswag	TinyLlama 1.1B Chat	Accuracy	45.69%	-
	Coven Tiny 1.1B	Accuracy	43.44%	-4.92%
GSM8K (Strict)	TinyLlama 1.1B Chat	Exact Match	1.82%	-
	Coven Tiny 1.1B	Exact Match	14.71%	+708.24%
GSM8K (Flexible)	TinyLlama 1.1B Chat	Exact Match	2.65%	-
	Coven Tiny 1.1B	Exact Match	14.63%	+452.08%
BoolQ	TinyLlama 1.1B Chat	Accuracy	58.69%	-
	Coven Tiny 1.1B	Accuracy	65.20%	+11.09%
ARC Easy	TinyLlama 1.1B Chat	Accuracy	66.54%	-
	Coven Tiny 1.1B	Accuracy	57.24%	-13.98%
ARC Challenge	TinyLlama 1.1B Chat	Accuracy	34.13%	-
	Coven Tiny 1.1B	Accuracy	34.81%	+1.99%
Humaneval	TinyLlama 1.1B Chat	Pass@1	10.98%	-
	Coven Tiny 1.1B	Pass@1	10.37%	-5.56%
Drop	TinyLlama 1.1B Chat	Score	16.02%	-
	Coven Tiny 1.1B	Score	16.36%	+2.12%
BBH	Coven Tiny 1.1B	Average	29.02%	-

💻 Usage

python

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="raidhon/coven_tiny_1.1b_32k_orpo_alpha", torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=2048, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

RichardErkhov/raidhon_-_coven_tiny_1.1b_32k_orpo_alpha-gguf