Instructions to use SvalTek/L3-CharThink-Base-Fix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SvalTek/L3-CharThink-Base-Fix with Transformers:

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

pipe = pipeline("text-generation", model="SvalTek/L3-CharThink-Base-Fix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SvalTek/L3-CharThink-Base-Fix")
model = AutoModelForCausalLM.from_pretrained("SvalTek/L3-CharThink-Base-Fix")
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 SvalTek/L3-CharThink-Base-Fix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SvalTek/L3-CharThink-Base-Fix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SvalTek/L3-CharThink-Base-Fix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SvalTek/L3-CharThink-Base-Fix

SGLang

How to use SvalTek/L3-CharThink-Base-Fix 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 "SvalTek/L3-CharThink-Base-Fix" \
    --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": "SvalTek/L3-CharThink-Base-Fix",
		"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 "SvalTek/L3-CharThink-Base-Fix" \
        --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": "SvalTek/L3-CharThink-Base-Fix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use SvalTek/L3-CharThink-Base-Fix with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SvalTek/L3-CharThink-Base-Fix to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SvalTek/L3-CharThink-Base-Fix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SvalTek/L3-CharThink-Base-Fix to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SvalTek/L3-CharThink-Base-Fix",
    max_seq_length=2048,
)

Docker Model Runner
How to use SvalTek/L3-CharThink-Base-Fix with Docker Model Runner:
```
docker model run hf.co/SvalTek/L3-CharThink-Base-Fix
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is tuned for character-POV internal cognition.

Its <think> blocks are intended to represent subjective in-character cognition: perception, memory, emotional appraisal, motive formation, and scene-state processing. They are not generic assistant planning or task-solving chain-of-thought.

For best results, give the model a system prompt that establishes an identity-bearing frame, such as a named persona, character card, role, narrator voice, or immersive writing mode. Minimal examples include:

You write immersive character responses.

You are {character name}, {brief identity/personality/context}.

Without an identity-bearing system prompt, the base assistant/RP prior may dominate and the model may produce ordinary visible roleplay without opening a <think> block.

Example output: System Prompt: You write immersive character responses.

Prompt:

You’re playing a character named Alex who is feeling nervous about meeting an old friend, Taylor, after several years. Alex doesn’t know what Taylor thinks of them anymore, and there’s a lot of unspoken history between them.

Output:

<think>
Oh god, it's really him. I can see his face through the window - Taylor. I haven't seen him in so long, not since that night. My heart is pounding like crazy, I can barely breathe. What does he think of me? Is he still mad about everything? I bet he is. I did some pretty stupid shit back then. Okay okay, deep breaths. Just act normal, don't let him see how nervous you are. He probably doesn't even remember half of it anyway. I hope he doesn't say my name too fast like that time. God, I wish I could just turn around and walk away. But it's too late now. He's already spotted me. What was I thinking coming here today?
</think>

*Alex sees Taylor approaching through the cafe window, a mix of familiar and uncomfortable emotions swirling inside them as they fumble with their backpack straps, trying to look at least a little composed. They manage a weak smile when Taylor reaches the door.* "Hey... um, hi." *They hesitate for a moment before extending a hand in tentative greeting.* "It's been a while."

Developed by: SvalTek
License: apache-2.0
Finetuned from model : SvalTek/L3-CharThink-Base-Test1

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 121

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for SvalTek/L3-CharThink-Base-Fix

Unable to build the model tree, the base model loops to the model itself. Learn more.