Instructions to use syntropy-ai/Soren-1-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use syntropy-ai/Soren-1-Small with Transformers:

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

pipe = pipeline("text-generation", model="syntropy-ai/Soren-1-Small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("syntropy-ai/Soren-1-Small")
model = AutoModelForCausalLM.from_pretrained("syntropy-ai/Soren-1-Small")
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 syntropy-ai/Soren-1-Small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/syntropy-ai/Soren-1-Small

SGLang

How to use syntropy-ai/Soren-1-Small 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 "syntropy-ai/Soren-1-Small" \
    --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": "syntropy-ai/Soren-1-Small",
		"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 "syntropy-ai/Soren-1-Small" \
        --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": "syntropy-ai/Soren-1-Small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use syntropy-ai/Soren-1-Small 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 syntropy-ai/Soren-1-Small 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 syntropy-ai/Soren-1-Small to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for syntropy-ai/Soren-1-Small to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="syntropy-ai/Soren-1-Small",
    max_seq_length=2048,
)

Docker Model Runner
How to use syntropy-ai/Soren-1-Small with Docker Model Runner:
```
docker model run hf.co/syntropy-ai/Soren-1-Small
```

Soren-1-Small / README.md

Andy-ML-And-AI

Add math patch note

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	---
	language:
	- en

	license: apache-2.0

	base_model:
	- Qwen/Qwen3.5-2B

	library_name: transformers

	pipeline_tag: text-generation

	tags:
	- soren
	- soren-1
	- soren-1-small
	- syntropy-ai
	- project-syntropic


	- chat
	- assistant
	- conversational
	- instruct
	- reasoning
	- thinking
	- long-context


	- qwen
	- qwen3
	- qwen3.5
	- qwen3.5-2b
	- transformer
	- decoder-only


	- sft
	- dpo
	- lora
	- merged-lora
	- unsloth
	- trl
	- preference-optimization
	- supervised-finetuning


	- coding
	- code-generation
	- code-assistant
	- software-engineering
	- python
	- reasoning-model
	- math
	- problem-solving
	- instruction-following
	- agentic
	- analytical

	- 1m-context
	- long-context-model
	- yarn
	- yarn-4x
	- 1048576-context

	- human-like
	- low-hallucination
	- honest-ai
	- helpful-assistant

	- rtx-pro-6000-blackwell
	- blackwell
	- nvidia

	- claude-style
	- reasoning-assistant
	- coding-model
	- compact-model
	- edge-ai
	- local-llm
	- open-weights
	- open-source-ai

	datasets:
	- syntropy-ai/Its-Me-Soren
	- Roman1111111/claude-sonnet-4.6-120000x
	- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
	- TeichAI/Claude-Opus-4.6-Reasoning-887x
	- dalisoft/claude-opus-4.6-high-reasoning-700x
	- TeichAI/claude-4.5-opus-high-reasoning-250x
	- Roman1111111/claude-opus-4.6-10000x
	- TeichAI/Claude-Sonnet-4.6-Reasoning-1100x
	- Hastagaras/Claude-Sonnet-X-Opus-4.6-Reasoning-small-500
	- TeichAI/lordx64-claude-opus-4.7-max-cleaned
	- ianncity/Hunter-Alpha-Programming-160000x
	- TeichAI/gpt-5-codex-250x
	- TeichAI/gpt-5.2-high-reasoning-250x
	- Nettoov/Gpt-5.4-Xhigh-Reasoning-2000x
	- nvidia/HelpSteer
	- m-a-p/CodeFeedback-Filtered-Instruction
	- Sashvat/HyperThink-X-Nvidia-Opencode-Reasoning-200K
	- openbmb/UltraFeedback
	- argilla/OpenHermesPreferences
	- argilla/distilabel-capybara-dpo-7k-binarized
	- Vezora/Code-Preference-Pairs
	- Code-Refinement/dpo-sample-perfect-less

	widget:
	- text: "Write a Python implementation of A* pathfinding."
	- text: "Explain special relativity like I'm 15."
	- text: "Debug this Rust code."
	- text: "Design a PostgreSQL schema for a social network."

	inference: false

	---

	# Soren-1-Small

	<div align="center">

	![Syntropy-AI](https://img.shields.io/badge/Syntropy--AI-Project%20Syntropic-F5C842?style=for-the-badge)
	![Base Model](https://img.shields.io/badge/Base-Qwen3.5--2B-blue?style=for-the-badge)
	![Context](https://img.shields.io/badge/Context-1M%20tokens-green?style=for-the-badge)
	![License](https://img.shields.io/badge/License-Apache%202.0-orange?style=for-the-badge)

	Soren is a fine-tuned AI assistant built to think before it speaks, write code that actually works, and talk like a person — not a product.

	</div>

	---

	## Note:
	Model's "math" patch is to be made(model got 60% on gsm8k hence we are making a math patch)


	## What is Soren?

	Soren is an AI assistant created by Andy at Syntropy-AI as part of Project Syntropic. It is built on Qwen3.5-2B and trained through a carefully sequenced pipeline of supervised fine-tuning and direct preference optimization to produce a model with four core strengths:

	- Reasoning first — Soren thinks through problems before answering. It uses extended chain-of-thought internally and surfaces its reasoning when it helps the user follow along.
	- Low hallucination — Soren does not invent facts, statistics, citations, or technical details. When it does not know something, it says so.
	- Honest code — Soren never fabricates APIs, libraries, or function signatures. It writes complete implementations, not stubs. No placeholder comments, no shortcuts.
	- Human-like tone — Soren does not open with "Certainly!" or "As an AI...". It communicates directly and warmly, the way a knowledgeable friend would.

	Soren-1-Small is the first release in the Soren-1 family. Medium (9B) and Large (27B) are planned.

	### Context Window

	Soren-1-Small supports a 1,048,576 token (~1M) context window via YaRN 4x extension applied post-training. The base Qwen3.5-2B native context is 262,144 tokens.

	---

	## Training Configuration

	\| Parameter \| Value \|
	\|---\|---\|
	\| Base model \| Qwen/Qwen3.5-2B \|
	\| Precision \| BF16 full LoRA (no quantization) \|
	\| LoRA rank \| 64 \|
	\| LoRA alpha \| 128 \|
	\| Target modules \| q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj \|
	\| Sequence length \| 4096–8192 (session dependent) \|
	\| Effective batch size \| 16 \|
	\| Optimizer \| adamw_torch \|
	\| Framework \| Unsloth + TRL \|
	\| Hardware \| NVIDIA RTX PRO 6000 Blackwell (96GB VRAM) \|

	---

	## Training Pipeline

	### SFT — Supervised Fine-Tuning

	Training followed a sequential LoRA chaining approach. Each session trains a LoRA adapter, merges it into the base model, and the next session trains on the merged result.

	Session 0 — Soren Identity v1
	- Dataset: `syntropy-ai/Its-Me-Soren` (200 examples)
	- LR: `5e-5` \| Epochs: 3
	- Purpose: Establish Soren's core identity and persona before any other training

	Session 1 — Reasoning Warmup
	- Dataset: `Roman1111111/claude-sonnet-4.6-120000x` (80k slice, capped at 250 steps)
	- LR: `2e-4`
	- Purpose: Broad instruction following and conversational quality foundation

	Session 2A — Claude Core (~26k examples, capped at 800 steps)
	- LR: `5e-5`
	- Datasets:
	- `angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k` (8.7k slice)
	- `TeichAI/Claude-Opus-4.6-Reasoning-887x`
	- `dalisoft/claude-opus-4.6-high-reasoning-700x`
	- `TeichAI/claude-4.5-opus-high-reasoning-250x`
	- `Roman1111111/claude-opus-4.6-10000x`
	- `TeichAI/Claude-Sonnet-4.6-Reasoning-1100x`
	- `Hastagaras/Claude-Sonnet-X-Opus-4.6-Reasoning-small-500`
	- `TeichAI/lordx64-claude-opus-4.7-max-cleaned`

	Session 2B — Code Core (~24.5k examples)
	- LR: `3e-5`
	- Datasets:
	- `ianncity/Hunter-Alpha-Programming-160000x` (2k slice)
	- `TeichAI/gpt-5-codex-250x`
	- `TeichAI/gpt-5.2-high-reasoning-250x`
	- `Nettoov/Gpt-5.4-Xhigh-Reasoning-2000x`
	- `nvidia/HelpSteer` (5k slice)
	- `m-a-p/CodeFeedback-Filtered-Instruction` (5k slice)
	- `Sashvat/HyperThink-X-Nvidia-Opencode-Reasoning-200K` (10k slice)

	Session 4 — Soren Identity v2 (Rescue)
	- Dataset: `syntropy-ai/Its-Me-Soren` (200 examples)
	- LR: `5e-5` \| Epochs: 3
	- Purpose: Reinforce Soren's identity after ~60k examples of third-party data

	---

	### DPO — Direct Preference Optimization

	DPO1 — General Preference (~34k examples)
	- LR: `1e-6` \| Beta: `0.1`
	- Datasets:
	- `openbmb/UltraFeedback` (17k slice)
	- `argilla/OpenHermesPreferences` (10k slice)
	- `argilla/distilabel-capybara-dpo-7k-binarized` (full, ~7k)

	DPO2 — Code Preference (~6k examples)
	- LR: `1e-6` \| Beta: `0.1`
	- Datasets:
	- `Vezora/Code-Preference-Pairs` (5k slice)
	- `Code-Refinement/dpo-sample-perfect-less` (full)

	---

	### Post-Training

	- YaRN 4x applied to `config.json` — extends context from 262k to ~1M tokens
	- Default system prompt baked into the chat template — Soren's identity, tone, and behavioral guidelines are always active without needing to pass a system message at inference time

	---

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"syntropy-ai/Soren-1-Small",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	tokenizer = AutoTokenizer.from_pretrained("syntropy-ai/Soren-1-Small")

	messages = [{"role": "user", "content": "Write a Python function to check if a number is prime."}]

	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	enable_thinking=True,
	)

	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	outputs = model.generate(
	**inputs,
	max_new_tokens=2048,
	temperature=0.7,
	do_sample=True,
	)
	print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	---

	## Credits

	Created by [Andy](https://github.com/Andy-ML-And-AI) at [Syntropy-AI](https://huggingface.co/syntropy-ai) — Project Syntropic

	Compute generously provided by [Lightning.ai](https://lightning.ai) — NVIDIA RTX PRO 6000 Blackwell (96GB VRAM)

	Training framework — [Unsloth](https://github.com/unslothai/unsloth) by Daniel Han-Chen and the Unsloth team

	Dataset authors — All dataset creators listed in the pipeline above. Thank you for making your data public.

	---

	## License

	Apache 2.0