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RichardErkhov/nfaheem_-_Marcoroni-7b-DPO-Merge-gguf
nfaheem_-_Marcoroni-7b-DPO-Merge-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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From the Hugging Face model README
Quantization made by Richard Erkhov.
Marcoroni-7b-DPO-Merge - GGUF
| Name | Quant method | Size |
|---|---|---|
| Marcoroni-7b-DPO-Merge.Q2_K.gguf | Q2_K | 2.53GB |
| Marcoroni-7b-DPO-Merge.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Marcoroni-7b-DPO-Merge.IQ3_S.gguf | IQ3_S | 2.96GB |
| Marcoroni-7b-DPO-Merge.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Marcoroni-7b-DPO-Merge.IQ3_M.gguf | IQ3_M | 3.06GB |
| Marcoroni-7b-DPO-Merge.Q3_K.gguf | Q3_K | 3.28GB |
| Marcoroni-7b-DPO-Merge.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Marcoroni-7b-DPO-Merge.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Marcoroni-7b-DPO-Merge.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Marcoroni-7b-DPO-Merge.Q4_0.gguf | Q4_0 | 3.83GB |
| Marcoroni-7b-DPO-Merge.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Marcoroni-7b-DPO-Merge.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Marcoroni-7b-DPO-Merge.Q4_K.gguf | Q4_K | 4.07GB |
| Marcoroni-7b-DPO-Merge.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Marcoroni-7b-DPO-Merge.Q4_1.gguf | Q4_1 | 4.24GB |
| Marcoroni-7b-DPO-Merge.Q5_0.gguf | Q5_0 | 4.65GB |
| Marcoroni-7b-DPO-Merge.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Marcoroni-7b-DPO-Merge.Q5_K.gguf | Q5_K | 4.78GB |
| Marcoroni-7b-DPO-Merge.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Marcoroni-7b-DPO-Merge.Q5_1.gguf | Q5_1 | 5.07GB |
| Marcoroni-7b-DPO-Merge.Q6_K.gguf | Q6_K | 5.53GB |
| Marcoroni-7b-DPO-Merge.Q8_0.gguf | Q8_0 | 7.17GB |
license: apache-2.0 tags:
Marcoroni-7b-DPO-Merge is a merge of the following models using mergekit and inspired by Maxime Labonne's work:
models:
- model: madatnlp/marcoroni-7b-v3-safetensor
# no parameters necessary for base model
- model: fblgit/UNA-TheBeagle-7b-v1
parameters:
density: 0.3
weight: 0.5
- model: udkai/Turdus
parameters:
density: 0.7
weight: 0.3
merge_method: ties
base_model: madatnlp/marcoroni-7b-v3-safetensor
parameters:
normalize: true
dtype: float16
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "nfaheem/Marcoroni-7b-DPO-Merge"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|
| 74.9 | 73.04 | 88.8 | 64.24 | 70.47 | 85.24 | 67.63 |
It's Ranked # 1 on HuggingFace Leaderboard among around 13B parameters (01/15/2024)
| Model | Average | ARC | HellaSwag | MMLU | Truthful QA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| nfaheem/Marcoroni-7b-DPO-Merge | 74.9 | 73.04 | 88.8 | 64.24 | 70.47 | 85.24 | 67.63 |
| mlabonne/Beagle14-7b | 74.76 | 72.95 | 87.95 | 64.7 | 68.38 | 82.64 | 71.42 |
| udkai/Turdus | 74.66 | 73.38 | 88.56 | 64.52 | 67.11 | 86.66 | 67.7 |
| CultriX/MergeTrix-7B | 74.33 | 72.24 | 87.84 | 64.88 | 66.27 | 83.5 | 71.19 |
| fblgit/UNA-TheBeagle-7b-v1 | 73.87 | 73.04 | 88 | 63.48 | 69.85 | 82.16 | 66.72 |
