Instructions to use OddTheGreat/Spring_24B_V.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OddTheGreat/Spring_24B_V.5 with Transformers:

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

pipe = pipeline("text-generation", model="OddTheGreat/Spring_24B_V.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OddTheGreat/Spring_24B_V.5")
model = AutoModelForCausalLM.from_pretrained("OddTheGreat/Spring_24B_V.5")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OddTheGreat/Spring_24B_V.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OddTheGreat/Spring_24B_V.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OddTheGreat/Spring_24B_V.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OddTheGreat/Spring_24B_V.5

SGLang

How to use OddTheGreat/Spring_24B_V.5 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 "OddTheGreat/Spring_24B_V.5" \
    --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": "OddTheGreat/Spring_24B_V.5",
		"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 "OddTheGreat/Spring_24B_V.5" \
        --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": "OddTheGreat/Spring_24B_V.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OddTheGreat/Spring_24B_V.5 with Docker Model Runner:
```
docker model run hf.co/OddTheGreat/Spring_24B_V.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spring_24B_V.5

This is a merge of pre-trained language models

This model is a byproduct of my experiments aimed to create model free from toxic positivity, good for "dark" playthroughs.

This model isn't fully reached my goal, and not too stable but it's interesting model.

First of all, model is close to neutrality towards user.

Model is uncensored (heretic'd mistral as base model), tested on erp, gore, swearings and hate speech. Important to say it was tested in rp scenario, not in straight task to model.

I've got hooked by smartness of this model. Really good for 24b, but not without halluciantions. In three swipes i usually achieved good responce.

Context attention also is good, nicely working with many lorebooks. Model actively utilises info from char card, at least up to 10k context in use. (10k is my limit for tests.)

Style of writing is prompt depending. Lenght, style and format depends on char card, first message, user input and sysprompt. In my case, it's been nice to read outputs. Variation of swipes is normal, but i've seen better.

Instructions are followed, mostly. For summarize and similar tasks it's better to lower temperature. On higher temperatures model becomes unstable. 0.8 was maximum for adequate responses.

RU was tested, good to play.

Tested on Mistral - tekken V7 preset, T0.81, xtc off. Modified Shingane-v1 sysprompt.

Downloads last month: 16

Safetensors

Model size

24B params

Tensor type

F16

Model tree for OddTheGreat/Spring_24B_V.5

Gryphe/Codex-24B-Small-3.2

LatitudeGames/Hearthfire-24B

OddTheGreat/Machina_24B.V2

Vikhrmodels/Vistral-24B-Instruct

arnomatic/Mistral-Small-3.2-24B-Instruct-2506-Text-Only-heretic

Merge model

this model

Quantizations

3 models