Instructions to use Daemontatox/SOCAM-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Daemontatox/SOCAM-V1 with Transformers:

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

pipe = pipeline("text-generation", model="Daemontatox/SOCAM-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Daemontatox/SOCAM-V1")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/SOCAM-V1")
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

vLLM

How to use Daemontatox/SOCAM-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Daemontatox/SOCAM-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Daemontatox/SOCAM-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Daemontatox/SOCAM-V1

SGLang

How to use Daemontatox/SOCAM-V1 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 "Daemontatox/SOCAM-V1" \
    --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": "Daemontatox/SOCAM-V1",
		"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 "Daemontatox/SOCAM-V1" \
        --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": "Daemontatox/SOCAM-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Daemontatox/SOCAM-V1 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 Daemontatox/SOCAM-V1 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 Daemontatox/SOCAM-V1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Daemontatox/SOCAM-V1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Daemontatox/SOCAM-V1",
    max_seq_length=2048,
)

Docker Model Runner
How to use Daemontatox/SOCAM-V1 with Docker Model Runner:
```
docker model run hf.co/Daemontatox/SOCAM-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Daemontatox/SOCAM-V1

Model Description

SOCAM-V1 (Social Cognitive Agent Model – V1) is a fine-tuned large language model built on top of Qwen/Qwen3-30B-A3B-Instruct.

The model is trained to function as a Cognitive State Machine, extracting cognitive chains from natural social utterances based on Theory of Mind (ToM) reasoning.

Each cognitive chain follows the structure:

Situation ⇒ Clue ⇒ Thought ⇒ (Action + Emotion)

This provides an interpretable representation of a user’s cognitive state, supporting applications in dialogue systems, emotional support agents, and multi-agent cognitive architectures.

Training Details

Base Model: Qwen/Qwen3-30B-A3B-Instruct

Fine-tuning Method: QLoRA with Unsloth + TRL

Dataset: Daemontatox/SOCAM

Adapted from the COKE dataset (Wu et al., 2024)

~45k structured samples with fields: situation, clue, thought, action, emotion

Emotions restricted to: Love, Surprise, Joyful, Sad, Angry, Fearful

Training Parameters:

Sequence length: 2048

LoRA config: r=16, alpha=32, dropout=0.01

Optimizer: AdamW (8-bit)

Effective batch size: 256 (16 × grad acc 16)

Learning rate: 2e-4 (cosine schedule, warmup ratio 0.02)

Epochs: 2

Hardware: H100-class GPU (8-bit quantization for feasibility)

Model Capabilities

Converts free-text utterances into structured cognitive chains.

Ensures separation of:

Situation (context domain)

Clue (triggering factor)

Thought (internal cognition)

Action (behavioral response)

Emotion (affective category)

Outputs deterministic JSON for easy downstream parsing.

⁶

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Daemontatox/SOCAM-V1",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/SOCAM-V1")

prompt = """Situation: "I have an important exam tomorrow."
Clue: "I have studied consistently for weeks."
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected output:

{
  "situation": "I have an important exam tomorrow.",
  "clue": "I have studied consistently for weeks.",
  "thought": "I believe I will perform well and feel confident.",
  "action": "I review lightly and get proper rest.",
  "emotion": "Joyful"
}

Limitations & Risks

The model may misclassify ambiguous emotions (e.g., Sad vs Fearful).

Outputs depend on the quality of the SOCAM dataset and may reflect dataset biases.

Not suitable for clinical or medical use.

Always validate JSON outputs before downstream use.

Intended Uses

Research on machine Theory of Mind (ToM).

Multi-agent cognitive architectures (Tracker, Updater, Reviewer, Responder).

Dialogue systems requiring interpretable cognitive reasoning.

Not intended for:

Clinical diagnostics

Sensitive decision-making without human oversight

Citation

If you use this model, please cite:

@misc{socam2025, title = {SOCAM-V1: A Cognitive State Machine for Theory of Mind Reasoning}, author = {Ammar Alnagar}, year = {2025}, howpublished = {\url{https://huggingface.co/Daemontatox/SOCAM-V1}} }

Acknowledgments

Base model: Qwen/Qwen3-30B-A3B-Instruct

Dataset foundation: COKE (Wu et al., 2024)

Training libraries: Unsloth, TRL, Hugging Face Transformers

Downloads last month: 6

Safetensors

Model size

31B params

Tensor type

BF16

Model tree for Daemontatox/SOCAM-V1

Base model

Qwen/Qwen3-30B-A3B-Instruct-2507

Finetuned

(78)

this model

Quantizations

2 models

Daemontatox
/

SOCAM-V1