Instructions to use Ttimofeyka/Tissint-14B-v1.1-128k-RP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ttimofeyka/Tissint-14B-v1.1-128k-RP with Transformers:

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

pipe = pipeline("text-generation", model="Ttimofeyka/Tissint-14B-v1.1-128k-RP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ttimofeyka/Tissint-14B-v1.1-128k-RP")
model = AutoModelForCausalLM.from_pretrained("Ttimofeyka/Tissint-14B-v1.1-128k-RP", 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 Ttimofeyka/Tissint-14B-v1.1-128k-RP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ttimofeyka/Tissint-14B-v1.1-128k-RP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ttimofeyka/Tissint-14B-v1.1-128k-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ttimofeyka/Tissint-14B-v1.1-128k-RP

SGLang

How to use Ttimofeyka/Tissint-14B-v1.1-128k-RP 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 "Ttimofeyka/Tissint-14B-v1.1-128k-RP" \
    --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": "Ttimofeyka/Tissint-14B-v1.1-128k-RP",
		"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 "Ttimofeyka/Tissint-14B-v1.1-128k-RP" \
        --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": "Ttimofeyka/Tissint-14B-v1.1-128k-RP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Ttimofeyka/Tissint-14B-v1.1-128k-RP with Docker Model Runner:
```
docker model run hf.co/Ttimofeyka/Tissint-14B-v1.1-128k-RP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tissint-14B-v1.1-128k-RP

The model is based on SuperNova-Medius (as the current best 14B model) with a 128k context with an emphasis on creativity, including NSFW and multi-turn conversations.

According to my tests, this finetune is much more stable with different samplers than the original model. Censorship and refusals have been reduced.

The model started to follow the system prompt better, and the responses in ChatML format with bad samplers stopped reaching 800+ tokens for no reason.

V1.1

I have increased the amount of dataset and added instructions mixed with NSFW RP, which in theory will improve the quality of the model.

Chat Template - ChatML

Samplers

Balance

Temp : 0.8 - 1.15
Min P : 0.1

Repetition Penalty : 1.02

DRY 0.8, 1.75, 2, 2048 (change to 4096 or more if needed)

Creativity

Temp : 1.15 - 1.5
Top P : 0.9

Repetition Penalty : 1.03

DRY 0.82, 1.75, 2, 2048 (change to 4096 or more if needed)

Downloads last month: 13

Safetensors

Model size

15B params

Tensor type

F16

Model tree for Ttimofeyka/Tissint-14B-v1.1-128k-RP

Base model

Qwen/Qwen2.5-14B

Finetuned

arcee-ai/SuperNova-Medius

Finetuned

Ttimofeyka/Tissint-14B-128k-RP

Finetuned

(2)

this model

Quantizations

2 models