Instructions to use saishf/Multi-Verse-RP-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Multi-Verse-RP-7B with Transformers:

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

pipe = pipeline("text-generation", model="saishf/Multi-Verse-RP-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saishf/Multi-Verse-RP-7B")
model = AutoModelForCausalLM.from_pretrained("saishf/Multi-Verse-RP-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 saishf/Multi-Verse-RP-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saishf/Multi-Verse-RP-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": "saishf/Multi-Verse-RP-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saishf/Multi-Verse-RP-7B

SGLang

How to use saishf/Multi-Verse-RP-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 "saishf/Multi-Verse-RP-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": "saishf/Multi-Verse-RP-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 "saishf/Multi-Verse-RP-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": "saishf/Multi-Verse-RP-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saishf/Multi-Verse-RP-7B with Docker Model Runner:
```
docker model run hf.co/saishf/Multi-Verse-RP-7B
```

Multi-Verse-RP-7B / README.md

saishf

Update README.md

c745030 verified over 2 years ago

3.73 kB

base_model:
  - ammarali32/multi_verse_model
  - jeiku/Theory_of_Mind_Roleplay_Mistral
  - ammarali32/multi_verse_model
  - jeiku/Alpaca_NSFW_Shuffled_Mistral
  - ammarali32/multi_verse_model
  - jeiku/Theory_of_Mind_Mistral
  - ammarali32/multi_verse_model
  - jeiku/Gnosis_Reformatted_Mistral
  - ammarali32/multi_verse_model
  - ammarali32/multi_verse_model
  - jeiku/Re-Host_Limarp_Mistral
  - ammarali32/multi_verse_model
  - jeiku/Luna_LoRA_Mistral
library_name: transformers
license: cc-by-nc-4.0
tags:
  - mergekit
  - merge
language:
  - en

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

This merge is entirely experimental, I've only tested it a few times but it seems to work? Thanks for all the loras jeiku. I keep getting driver crashes training my own :\
Update, It scores well! My highest scoring model so far

Merge Method

This model was merged using the task arithmetic merge method using ammarali32/multi_verse_model as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: task_arithmetic
base_model: ammarali32/multi_verse_model
parameters:
  normalize: true
models:
  - model: ammarali32/multi_verse_model+jeiku/Gnosis_Reformatted_Mistral
    parameters:
      weight: 0.7
  - model: ammarali32/multi_verse_model+jeiku/Theory_of_Mind_Roleplay_Mistral
    parameters:
      weight: 0.65
  - model: ammarali32/multi_verse_model+jeiku/Luna_LoRA_Mistral
    parameters:
      weight: 0.5
  - model: ammarali32/multi_verse_model+jeiku/Re-Host_Limarp_Mistral
    parameters:
      weight: 0.8
  - model: ammarali32/multi_verse_model+jeiku/Alpaca_NSFW_Shuffled_Mistral
    parameters:
      weight: 0.75  
  - model: ammarali32/multi_verse_model+jeiku/Theory_of_Mind_Mistral
    parameters:
      weight: 0.7                     
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.73
AI2 Reasoning Challenge (25-Shot)	72.35
HellaSwag (10-Shot)	88.37
MMLU (5-Shot)	63.94
TruthfulQA (0-shot)	73.19
Winogrande (5-shot)	84.14
GSM8k (5-shot)	66.41