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RichardErkhov/mayacinka_-_yam-jom-7B-passthrough-v2-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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yam-jom-7B-passthrough-v2 - GGUF

  • —Model creator: https://huggingface.co/mayacinka/
  • —Original model: https://huggingface.co/mayacinka/yam-jom-7B-passthrough-v2/

Original model description: --- tags:

  • —merge
  • —mergekit
  • —lazymergekit
  • —yam-peleg/Experiment26-7B
  • —eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
  • —yam-peleg/Experiment26-7B
  • —eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
  • —yam-peleg/Experiment26-7B
  • —eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
  • —yam-peleg/Experiment26-7B base_model:
  • —yam-peleg/Experiment26-7B
  • —eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 license: apache-2.0 ---

test_passthrough

test_passthrough is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml
dtype: float16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 8]
    model: yam-peleg/Experiment26-7B
- sources:
  - layer_range: [4, 12]
    model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
- sources:
  - layer_range: [8, 16]
    model: yam-peleg/Experiment26-7B
- sources:
  - layer_range: [12, 20]
    model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
- sources:
  - layer_range: [16, 24]
    model: yam-peleg/Experiment26-7B
- sources:
  - layer_range: [20, 28]
    model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
- sources:
  - layer_range: [24, 32]
    model: yam-peleg/Experiment26-7B

💻 Usage

python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/test_passthrough"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])