RichardErkhov/aloobun_-_Reyna-Mini-1.8B-v0.2-gguf

Quantization made by Richard Erkhov.

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Reyna-Mini-1.8B-v0.2 - GGUF

• Model creator: https://huggingface.co/aloobun/
• Original model: https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2/

Name	Quant method	Size
Reyna-Mini-1.8B-v0.2.Q2_K.gguf	Q2_K	0.79GB
Reyna-Mini-1.8B-v0.2.Q3_K_S.gguf	Q3_K_S	0.89GB
Reyna-Mini-1.8B-v0.2.Q3_K.gguf	Q3_K	0.95GB
Reyna-Mini-1.8B-v0.2.Q3_K_M.gguf	Q3_K_M	0.95GB
Reyna-Mini-1.8B-v0.2.Q3_K_L.gguf	Q3_K_L	0.98GB
Reyna-Mini-1.8B-v0.2.IQ4_XS.gguf	IQ4_XS	1.01GB
Reyna-Mini-1.8B-v0.2.Q4_0.gguf	Q4_0	1.04GB
Reyna-Mini-1.8B-v0.2.IQ4_NL.gguf	IQ4_NL	1.05GB
Reyna-Mini-1.8B-v0.2.Q4_K_S.gguf	Q4_K_S	1.08GB
Reyna-Mini-1.8B-v0.2.Q4_K.gguf	Q4_K	1.13GB
Reyna-Mini-1.8B-v0.2.Q4_K_M.gguf	Q4_K_M	1.13GB
Reyna-Mini-1.8B-v0.2.Q4_1.gguf	Q4_1	1.13GB
Reyna-Mini-1.8B-v0.2.Q5_0.gguf	Q5_0	1.22GB
Reyna-Mini-1.8B-v0.2.Q5_K_S.gguf	Q5_K_S	1.24GB
Reyna-Mini-1.8B-v0.2.Q5_K.gguf	Q5_K	1.28GB
Reyna-Mini-1.8B-v0.2.Q5_K_M.gguf	Q5_K_M	1.28GB
Reyna-Mini-1.8B-v0.2.Q5_1.gguf	Q5_1	1.31GB
Reyna-Mini-1.8B-v0.2.Q6_K.gguf	Q6_K	1.47GB
Reyna-Mini-1.8B-v0.2.Q8_0.gguf	Q8_0	1.82GB

Original model description:

license: other library_name: transformers tags: - chatml - finetune - gpt4 - synthetic data - custom_code - qwen2 datasets: - Locutusque/Hercules-v3.0 license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE model-index: - name: Reyna-Mini-1.8B-v0.2 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 36.6 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 60.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 44.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 41.24 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 61.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 31.31 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard

• Finetuned Qwen/Qwen1.5-1.8B-Chat, with SFT on Hercules v3 dataset.
• This marks the third model in this series.
• Format: ChatML -

    <|im_start|>system
    {system}<|im_end|>
    <|im_start|>user
    {prompt}<|im_end|>
    <|im_start|>assistant
  

• Next step would be to do a DPO train on top.

Benchamrks:

Avg.	Arc	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
45.94	36.6	60.19	44.75	41.24	61.56	31.31

Example:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
import torch

class MyStoppingCriteria(StoppingCriteria):
  def __init__(self, target_sequence, prompt):
    self.target_sequence = target_sequence
    self.prompt=prompt

  def __call__(self, input_ids, scores, **kwargs):
    generated_text = tokenizer.decode(input_ids[0])
    generated_text = generated_text.replace(self.prompt,'')
    if self.target_sequence in generated_text:
        return True 
    return False 

  def __len__(self):
    return 1

  def __iter__(self):
    yield self

modelpath="aloobun/Reyna-Mini-1.8B-v0.2"

model = AutoModelForCausalLM.from_pretrained(
    modelpath,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,       
)

tokenizer = AutoTokenizer.from_pretrained(
    modelpath,
    trust_remote_code=True,      
    use_fast=False,
)

prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nIs there inherent order in nature or is it all chaos and chance?<|im_end|>\n<|im_start|>assistant\n"

encoded_input = tokenizer(prompt, return_tensors='pt')
input_ids=encoded_input['input_ids'].cuda()
streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
op = model.generate(
    input_ids,
    streamer=streamer,
    pad_token_id=tokenizer.eos_token_id,
    do_sample=True,
    temperature=0.6,
    top_p=0.8,
    max_new_tokens=512,
    stopping_criteria=MyStoppingCriteria("<|im_end|>", prompt)
)

Output:

Nature appears to be inherently organized, with patterns and structures that can be observed across different levels of organization. However, the exact mechanisms by which these patterns emerge and evolve remain largely unknown. The universe seems to be governed by a series of laws and principles known as "laws of physics," such as Newton's laws of motion, electromagnetism, and thermodynamics. These laws govern how matter and energy interact with each other and how they behave over time. Despite our understanding of these laws, we still struggle to comprehend the underlying mechanisms that allow for the emergence of complex patterns and structures. This is because the universe operates on a scale that is too small for us to observe directly, and therefore we cannot fully understand its internal workings. In summary, while there may be some level of order and structure within the universe, the precise mechanisms governing this order remain largely unknown.<|im_end|>

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	45.94
AI2 Reasoning Challenge (25-Shot)	36.60
HellaSwag (10-Shot)	60.19
MMLU (5-Shot)	44.75
TruthfulQA (0-shot)	41.24
Winogrande (5-shot)	61.56
GSM8k (5-shot)	31.31