Instructions to use kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2

SGLang

How to use kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 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 "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2" \
    --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": "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2",
		"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 "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2" \
        --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": "kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2
```

TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2 / README.md

kim512

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	---
	tags:
	- merge
	- mergekit
	- lazymergekit
	- jdqwoi/TooManyMixRolePlay-7B-Story_V2
	- jdqwoi/TooManyMixRolePlay-7B-Story_V3
	base_model:
	- jdqwoi/TooManyMixRolePlay-7B-Story_V2
	- jdqwoi/TooManyMixRolePlay-7B-Story_V3
	---


	# EXL2 quants of [jdqwoi/TooManyMixRolePlay-7B-Story_V3.5](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story_V3.5)

	[6.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V3.5-6.0bpw-h6-exl2)
	[8.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story_V3.5-8.0bpw-h8-exl2)

	Created using the defaults from exllamav2 0.1.3 convert.py
	6.0bpw head bits = 6
	8.0bpw head bits = 8
	length = 8192
	dataset rows = 200
	measurement rows = 32
	measurement length = 8192

	# TooManyMixRolePlay-7B-Story_V3.5

	TooManyMixRolePlay-7B-Story_V3.5 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [jdqwoi/TooManyMixRolePlay-7B-Story_V2](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story_V2)
	* [jdqwoi/TooManyMixRolePlay-7B-Story_V3](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story_V3)

	## 🧩 Configuration

	```yaml
	slices:
	- sources:
	- model: jdqwoi/TooManyMixRolePlay-7B-Story_V2
	layer_range: [0, 32]
	- model: jdqwoi/TooManyMixRolePlay-7B-Story_V3
	layer_range: [0, 32]
	merge_method: slerp
	base_model: jdqwoi/TooManyMixRolePlay-7B-Story_V2
	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

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "jdqwoi/TooManyMixRolePlay-7B-Story_V3.5"
	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"])
	```