README.md

---
license: mit
datasets:
- a-m-team/AM-DeepSeek-R1-Distilled-1.4M
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-V3.2
pipeline_tag: text-to-image
library_name: adapter-transformers
tags:
- agent
---

# 🧠 SyntraLLM
**SyntraLLM** is a packaged and redistributed version of an open-source, high-performance
reasoning language model based on the **DeepSeek R1** architecture and weights.

This repository provides:
- Full model weights in Hugging Face–compatible format  
- Tokenizer & configs
- Example inference code (Python + Node.js)
- Ready-to-deploy structure for chatbots, agents, and backend systems

SyntraLLM is intended for the Syntra ecosystem, enabling seamless LLM integration across
applications, automation flows, and developer tools.

---

## 🔍 Base Model
This model is derived from the open-source **DeepSeek R1** release.

- Original Authors: *DeepSeek*  
- License: See included `LICENSE` file (DeepSeek R1 license applies)  
- Architecture: R1 (reasoning-focused causal transformer)

No modifications are made to the model’s weights or architecture.  
Only packaging, structure, naming, and documentation have been adapted for Syntra distribution.

---

## ✨ Features
- ⚡ High-performance reasoning and chain-of-thought generation  
- 📦 HuggingFace-compatible folder structure  
- 🔧 Clean, predictable inference behavior  
- 🧩 Suitable for agents, tools, automation, and backend AI services  
- 🔌 Works on CPU, GPU, and cloud inference backends  
- 🧰 Supports vLLM, TGI, and local Transformers  

---

## 📦 Repository Contents
config.json
tokenizer.json
tokenizer_config.json
generation_config.json
model.safetensors
README.md (this file)
LICENSE (original license)


---

## 🚀 Quick Start (Python)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "syntra-dev/SyntraLLM"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "Explain how a blockchain works in simple terms."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))


🟦 Quick Start (Node.js – transformers.js)
import { pipeline } from "@xenova/transformers";

const pipe = await pipeline(
  "text-generation",
  "syntra-dev/SyntraLLM"
);

const out = await pipe("What is quantum entanglement?", {
  max_new_tokens: 200
});

console.log(out[0].generated_text);

🔥 Chat Template

SyntraLLM is compatible with standard chat formatting:

<user>
Your question here...
</user>

<assistant>


Example:

prompt = "<user>\nGive me a 1 paragraph summary of Solana.\n</user>\n<assistant>"

🧪 Example Prompting (Reasoning Style)
<user>
Solve this: If train A travels 30 km in 20 minutes and train B travels 45 km in 30 minutes, which one is faster?
Show step-by-step reasoning.
</user>
<assistant>

⚠️ Limitations

SyntraLLM inherits all limitations of the base model, including:

Possible hallucinations

Potential for generating inaccurate or unsafe content

Lack of domain-specific training

Biases present in the original model

Syntra does not modify or fine-tune the base model.

🔒 Safety & Responsible Use

Do not rely on the model for factual decision-making without verification

Not suitable for medical, financial, or legal advice

Further safety fine-tuning is recommended before production deployment

For production environments, consider:

Output moderation

Rule-based filtering

Reinforcement learning with safety datasets

📄 License

This repository redistributes the original model under the DeepSeek-R1 license.
See LICENSE for full terms.
SyntraLLM only repackages and distributes the model, and does not claim training ownership.

🏷 Maintainer

Syntra Dev Team
HuggingFace: https://huggingface.co/syntra-dev

You are welcome to contribute extensions, tools, or integrations!