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RichardErkhov/
crumb_-_apricot-wildflower-20-gguf

Name: crumb_-_apricot-wildflower-20-gguf
Availability: InStock

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gguf

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apricot-wildflower-20 - GGUF

Model creator: https://huggingface.co/crumb/
Original model: https://huggingface.co/crumb/apricot-wildflower-20/

Name	Quant method	Size
apricot-wildflower-20.Q2_K.gguf	Q2_K	2.53GB
apricot-wildflower-20.IQ3_XS.gguf	IQ3_XS	2.81GB
apricot-wildflower-20.IQ3_S.gguf	IQ3_S	2.96GB
apricot-wildflower-20.Q3_K_S.gguf	Q3_K_S	2.95GB
apricot-wildflower-20.IQ3_M.gguf	IQ3_M	3.06GB
apricot-wildflower-20.Q3_K.gguf	Q3_K	3.28GB
apricot-wildflower-20.Q3_K_M.gguf	Q3_K_M	3.28GB
apricot-wildflower-20.Q3_K_L.gguf	Q3_K_L	3.56GB
apricot-wildflower-20.IQ4_XS.gguf	IQ4_XS	3.67GB
apricot-wildflower-20.Q4_0.gguf	Q4_0	3.83GB
apricot-wildflower-20.IQ4_NL.gguf	IQ4_NL	3.87GB
apricot-wildflower-20.Q4_K_S.gguf	Q4_K_S	3.86GB
apricot-wildflower-20.Q4_K.gguf	Q4_K	4.07GB
apricot-wildflower-20.Q4_K_M.gguf	Q4_K_M	4.07GB
apricot-wildflower-20.Q4_1.gguf	Q4_1	4.24GB
apricot-wildflower-20.Q5_0.gguf	Q5_0	4.65GB
apricot-wildflower-20.Q5_K_S.gguf	Q5_K_S	4.65GB
apricot-wildflower-20.Q5_K.gguf	Q5_K	4.78GB
apricot-wildflower-20.Q5_K_M.gguf	Q5_K_M	4.78GB
apricot-wildflower-20.Q5_1.gguf	Q5_1	5.07GB
apricot-wildflower-20.Q6_K.gguf	Q6_K	5.53GB
apricot-wildflower-20.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 model-index:

name: apricot-wildflower-20 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: 59.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 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: 81.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 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: 63.38 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 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: 41.76 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 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: 77.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 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: 33.97 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=crumb/apricot-wildflower-20 name: Open LLM Leaderboard

apricot-wildflower-20

This model is the Mistral-7b model finetuned for 1k steps with a combined lm loss and distillation loss on Openwebtext2 with a >=20 reddit score filter with training logits from Mixtral. I'm not going to pretend it was a big project I did it in a dream and woke up and replicated the code without any actual reason, idk how well it fares in benchmarks.

(update: not very good)

model	avg	arc	hellaswag	mmlu	truthfulqa	winogrande	gsm8k
apricot-wildflower-20	59.74	59.64	81.76	63.38	41.76	77.9	33.97
mistralai/Mistral-7B-v0.1	60.97	59.98	83.31	64.16	42.15	78.37	37.83

use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "crumb/apricot-wildflower-20"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", load_in_8bit=True)

text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Hello my name is Katie and I am a 20 year old student from the UK. I am currently studying for a degree in English Literature and Creative Writing at the University of Leeds. I am a huge fan of the Harry Potter series and have been since I was 10 years old. I have read the books countless times and have seen the films many times too. I am a huge fan of the Harry Potter fandom and have been a member of the Harry Potter forums for a few years now. I am also a member of the Harry Potter fan club and have been for a few years now. I

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	59.74
AI2 Reasoning Challenge (25-Shot)	59.64
HellaSwag (10-Shot)	81.76
MMLU (5-Shot)	63.38
TruthfulQA (0-shot)	41.76
Winogrande (5-shot)	77.90
GSM8k (5-shot)	33.97

RichardErkhov/crumb_-_apricot-wildflower-20-gguf

Original model description:

apricot-wildflower-20

use

Open LLM Leaderboard Evaluation Results

RichardErkhov/
crumb_-_apricot-wildflower-20-gguf