Instructions to use rmdhirr/Multiparadigm_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rmdhirr/Multiparadigm_7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rmdhirr/Multiparadigm_7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rmdhirr/Multiparadigm_7B")
model = AutoModelForCausalLM.from_pretrained("rmdhirr/Multiparadigm_7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rmdhirr/Multiparadigm_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rmdhirr/Multiparadigm_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Multiparadigm_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rmdhirr/Multiparadigm_7B

SGLang

How to use rmdhirr/Multiparadigm_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 "rmdhirr/Multiparadigm_7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Multiparadigm_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "rmdhirr/Multiparadigm_7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rmdhirr/Multiparadigm_7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rmdhirr/Multiparadigm_7B with Docker Model Runner:
```
docker model run hf.co/rmdhirr/Multiparadigm_7B
```

Multiparadigm_7B / README.md

rmdhirr

Update README.md

3e7fdc7 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

4.47 kB

	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	tags:
	- merge
	- mergekit
	- mistral
	- roleplay
	- MTSAIR/multi_verse_model
	- ResplendentAI/Paradigm_7B
	base_model:
	- MTSAIR/multi_verse_model
	- ResplendentAI/Paradigm_7B
	model-index:
	- name: Multiparadigm_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=rmdhirr/Multiparadigm_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: 88.95
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rmdhirr/Multiparadigm_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: 64.28
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rmdhirr/Multiparadigm_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: 76.87
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rmdhirr/Multiparadigm_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: 83.82
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rmdhirr/Multiparadigm_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: 69.37
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rmdhirr/Multiparadigm_7B
	name: Open LLM Leaderboard
	---
	<img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65ad2502043d53781aad2ee4%2FkmTOHCPDzyflcIrXPzgT5.png%26quot%3B%3C%2Fspan%3E alt="image" width="540" height="540" style="margin-bottom: 30px;">

	# 🌠 Multiparadigm_7B
	Multiparadigm_7B is a merge of the following models:
	* [MTSAIR/multi_verse_model](https://huggingface.co/MTSAIR/multi_verse_model)
	* [ResplendentAI/Paradigm_7B](https://huggingface.co/ResplendentAI/Paradigm_7B)

	# Quantizations
	Thanks to mradermacher, static GGUF quants are available [here](https://huggingface.co/mradermacher/Multiparadigm_7B-GGUF).

	# Configuration
	```yaml
	slices:
	- sources:
	- model: MTSAIR/multi_verse_model
	layer_range: [0, 32]
	- model: ResplendentAI/Paradigm_7B
	layer_range: [0, 32]
	merge_method: slerp
	base_model: MTSAIR/multi_verse_model
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.6, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.6, 0.7, 0.3, 0]
	- value: 0.6
	dtype: bfloat16
	```
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rmdhirr__Multiparadigm_7B)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|76.08\|
	\|AI2 Reasoning Challenge (25-Shot)\|73.21\|
	\|HellaSwag (10-Shot) \|88.95\|
	\|MMLU (5-Shot) \|64.28\|
	\|TruthfulQA (0-shot) \|76.87\|
	\|Winogrande (5-shot) \|83.82\|
	\|GSM8k (5-shot) \|69.37\|