Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
liminerity/Omningotex-7b-slerp
eren23/merged-dpo-binarized-NeutrixOmnibe-7B
Eval Results (legacy)
text-generation-inference
Instructions to use paulml/DPOB-INMTOB-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paulml/DPOB-INMTOB-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="paulml/DPOB-INMTOB-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("paulml/DPOB-INMTOB-7B") model = AutoModelForCausalLM.from_pretrained("paulml/DPOB-INMTOB-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use paulml/DPOB-INMTOB-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "paulml/DPOB-INMTOB-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "paulml/DPOB-INMTOB-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/paulml/DPOB-INMTOB-7B
- SGLang
How to use paulml/DPOB-INMTOB-7B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "paulml/DPOB-INMTOB-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "paulml/DPOB-INMTOB-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "paulml/DPOB-INMTOB-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "paulml/DPOB-INMTOB-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use paulml/DPOB-INMTOB-7B with Docker Model Runner:
docker model run hf.co/paulml/DPOB-INMTOB-7B
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Download README.md from paulml/DPOB-INMTOB-7B: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/paulml/DPOB-INMTOB-7B/resolve/main/README.md
- Command line
-
hf download hf://paulml/DPOB-INMTOB-7B/README.md
-
curl -L -o README.md https://huggingface.co/paulml/DPOB-INMTOB-7B/resolve/main/README.md
4.98 kB
metadata
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- liminerity/Omningotex-7b-slerp
- eren23/merged-dpo-binarized-NeutrixOmnibe-7B
base_model:
- liminerity/Omningotex-7b-slerp
- eren23/merged-dpo-binarized-NeutrixOmnibe-7B
model-index:
- name: DPOB-INMTOB-7B
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: 73.21
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
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: 89
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
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: 64.54
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
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: 76.6
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
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: 84.69
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
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: 69.22
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B
name: Open LLM Leaderboard
DPOB-INMTOB-7B
DPOB-INMTOB-7B is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: liminerity/Omningotex-7b-slerp
layer_range: [0, 32]
- model: eren23/merged-dpo-binarized-NeutrixOmnibe-7B
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/Omningotex-7b-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "paulml/DPOB-INMTOB-7B"
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"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 76.21 |
| AI2 Reasoning Challenge (25-Shot) | 73.21 |
| HellaSwag (10-Shot) | 89.00 |
| MMLU (5-Shot) | 64.54 |
| TruthfulQA (0-shot) | 76.60 |
| Winogrande (5-shot) | 84.69 |
| GSM8k (5-shot) | 69.22 |