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Model: neuracoder/neura-fa-en-1.9b Source: Original Platform
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README.md
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README.md
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---
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language:
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- fa
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- conversational
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- text-generation
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- persian
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- farsi
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- bilingual
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- lightweight
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- cpu
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- iranian-company
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- neuracoder
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- research-model
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pipeline_tag: text-generation
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datasets:
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- parsinlp/fa-chat
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- openchat_sharegpt4_fa
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metrics:
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- perplexity
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- rouge
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- bleu
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---
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# 🗣️ Neura-FA-EN-1.9B
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**Neura-FA-EN-1.9B** is an open‑source, ultra‑lightweight **bilingual conversational model** developed by the **Neuracoder** team (a leading Iranian AI company). With **1.9 billion parameters** and based on the modern **Qwen2 architecture** (architecture only – not derived from any existing Qwen model), it is specifically designed for **natural, fast, and local conversation** in **Persian (Farsi) and English**.
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Unlike giant multilingual models that require heavy GPUs or cloud APIs, Neura‑FA‑EN runs smoothly on **laptops, CPU‑only systems, and even Raspberry Pi**. It gives Persian‑speaking developers, researchers, and hobbyists a powerful offline assistant for everyday questions, simple summarization, informal translation, and general knowledge retrieval – all under a permissive Apache 2.0 license.
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---
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## ✨ Key Features
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- **Truly bilingual (Persian + English)** – Understands and generates both languages fluently, with natural code‑switching (e.g., “بگو hello world به انگلیسی”).
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- **Ultra‑lightweight** – Only 1.9B parameters, ~1.6 GB (FP16) / ~0.9 GB (INT8). Runs on 4 GB RAM devices.
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- **Offline & private** – No internet connection or API key needed after download.
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- **Fast inference** – 40–60 tok/s on T4 GPU, 8–12 tok/s on Intel i7 CPU, 2–3 tok/s on Raspberry Pi 4.
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- **Long context** – 32,768 tokens (≈24,000 Persian words), enough for long conversations or short stories.
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- **Iranian‑made, Apache 2.0** – Free for commercial and personal use, with full transparency.
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- **Research‑friendly** – Released as a **research model** to help the Persian AI community fine‑tune, quantise, or build upon it.
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---
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## 🎯 Suitable Use Cases
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- **Daily chit‑chat** – Casual conversation, small talk, jokes, and friendly assistant tasks.
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- **Simple Q&A** – Answering general knowledge questions (e.g., “پایتخت فرانسه کجاست?” / “What is the capital of France?”).
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- **Informal translation** – Translating short sentences or phrases between Persian and English (not professional/legal grade).
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- **Light summarisation** – Summarising a paragraph or a short article in Persian or English.
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- **Brainstorming & writing help** – Generating ideas, rewriting a sentence, fixing simple grammar.
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- **Educational tool for language learning** – Practicing Persian or English conversations (basic to intermediate level).
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- **Offline assistant for edge devices** – Embedded in chatbots, local web UIs, or Telegram bots (simple integration).
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### ❌ Not suitable for:
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- Code generation, debugging, or programming assistance.
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- Complex mathematical reasoning or multi‑step logic.
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- Professional translation (e.g., legal, medical).
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- Long document processing (>32k tokens).
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- Any task requiring up‑to‑date information after mid‑2024.
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---
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## 📊 Evaluation & Performance Metrics
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We evaluated Neura‑FA‑EN‑1.9B on standard Persian and English benchmarks for conversational models.
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| Dataset | Metric | Score | Note |
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|----------------------|--------------|---------|-------------------------------------------|
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| ParsiMMLU (5‑shot) | Accuracy | 48.7% | General knowledge in Persian |
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| PersianQA | Exact Match | 56.2% | Reading comprehension (questions in Persian) |
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| MMLU (English, 5‑shot)| Accuracy | 51.3% | General knowledge in English |
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| XNLI (fa) | Accuracy | 62.1% | Natural language inference (Persian) |
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| XNLI (en) | Accuracy | 68.5% | Natural language inference (English) |
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| Perplexity (fa‑wikitext)| PPL | 18.3 | Fluency on Persian texts |
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> **Interpretation:** The model performs on par with much larger multilingual models (e.g., XLM‑R 3B) on Persian tasks while being 40% smaller. For English, it stays competitive with dedicated 1.5B models.
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---
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## 📈 Comparison with Similar‑Sized Models
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| Model | Params | Persian MMLU | English MMLU | VRAM (FP16) | Speed (tok/s, T4) | License |
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|---------------------------|--------|--------------|--------------|-------------|-------------------|------------|
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| **Neura-FA-EN-1.9B** | 1.9B | **48.7%** | 51.3% | ~3.8 GB | 48 | Apache 2.0 |
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| Arian‑2B (Persian) | 2.0B | 44.2% | 28.7% | ~4.0 GB | 45 | Apache 2.0 |
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| Phi‑2 (2.7B, English‑only)| 2.7B | N/A | 57.8% | ~5.4 GB | 40 | MIT |
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| Gemma‑2B (English‑only) | 2.0B | N/A | 52.6% | ~4.0 GB | 52 | Gemma |
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> **Key points:** Neura‑FA‑EN is the **only 1.9B model** that provides strong performance on **both** Persian and English.
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---
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## 🧪 Technical Details & Training Process
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Built on the **Qwen2 architecture** (only the architecture, not derived from any existing model) and trained from scratch by Neuracoder.
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### Architecture
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- **Layers:** 28 decoder‑only layers.
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- **Attention:** Grouped Query Attention (GQA) – 12 query heads, 2 key/value heads.
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- **Activation:** SwiGLU.
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- **Context length:** 32,768 tokens.
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- **Embedding size:** 2048.
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- **Intermediate size:** 5632.
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### Pre‑training
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- **Data:** 350 billion tokens – 60% Persian (web texts, books, news, forums), 35% English (common crawl, books, Wikipedia), 5% code (to preserve basic formatting).
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- **Duration:** 18 days on 8× NVIDIA A100 (80GB) using DeepSpeed ZeRO‑3.
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- **Hyperparameters:** AdamW (lr=3e-4), cosine decay, warmup 2000 steps, batch size 512, seq len 2048 (later extended to 8192 with RoPE scaling).
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### Supervised Fine‑Tuning (SFT)
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- **Data:** 150,000 conversation pairs in Persian and English:
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- 80,000 from public Persian chat datasets (ParsiNLU, FaChat).
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- 50,000 from translated and cleaned ShareGPT data.
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- 20,000 hand‑written by Neuracoder team for natural code‑switching and cultural relevance.
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- **Format:** `{"system": "You are a helpful assistant.", "user": "...", "assistant": "..."}`
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- **Hyperparameters:** 3 epochs, lr=1e-5, batch size 128, LoRA (rank=32) then full fine‑tune last 6 layers.
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### Validation
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- Every 500 steps evaluated on held‑out Persian and English test sets.
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- Final checkpoint chosen by lowest perplexity on Persian validation and highest MMLU score.
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---
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## ⚡ Inference Speed & Hardware Requirements
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| Hardware | Weight format | Avg tokens/sec (gen 256 tokens) | Memory usage |
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|--------------------------|---------------|----------------------------------|---------------|
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| NVIDIA A100 (40GB) | FP16 | 78 tok/s | 4.1 GB |
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| NVIDIA T4 (16GB) | FP16 | 48 tok/s | 3.9 GB |
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| NVIDIA T4 (16GB) | INT8 | 55 tok/s | 2.3 GB |
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| NVIDIA GTX 1060 (6GB) | FP16 | 28 tok/s | 3.9 GB |
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| CPU (Intel i7-12700K) | INT8 | 9 tok/s | 2.1 GB |
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| Raspberry Pi 4 (4GB) | INT8 (ONNX) | 2–3 tok/s | 1.6 GB |
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> **Recommendation:** Use FP16 on any GPU with 6+ GB VRAM. For CPU or low‑memory devices, use INT8 quantised version (available separately).
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---
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## 🚀 Usage Guide
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### Installation
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```
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pip install transformers torch accelerate sentencepiece
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```
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### Example 1: Basic Persian conversation
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "neuracoder/neura-fa-en-1.9b"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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prompt = "به نظرت بهترین راه برای یادگیری زبان انگلیسی چیه؟"
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### Example 2: Mixed Persian‑English query
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```
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prompt = "یه جمله انگلیسی بنویس که معنی 'خورشید میتابد' رو برسونه"
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.6)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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### Example 3: Simple summarisation (English)
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```
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article = """The Persian cat is a long-haired breed characterized by its round face and short muzzle.
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It is one of the oldest cat breeds, originating from Persia (modern-day Iran)."""
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prompt = f"Summarise the following text in one sentence:\n\n{article}"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=80, temperature=0.3)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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### Download the model directly
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```
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git lfs install
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git clone https://huggingface.co/neuracoder/neura-fa-en-1.9b
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```
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Or via Python:
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```
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="neuracoder/neura-fa-en-1.9b", local_dir="./neura-fa-en-1.9b")
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```
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---
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## ⚠️ Limitations
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- **Not a code model** – Cannot write or debug programs reliably.
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- **Not a mathematical engine** – Struggles with multi‑step arithmetic or symbolic reasoning.
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- **Knowledge cutoff** – Mid‑2024. Unaware of very recent events or new APIs.
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- **Persian dialect** – Trained on standard Persian (Farsi); may not understand Dari or Tajik well.
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- **Formal translation** – Not suitable for legal, medical, or highly technical documents.
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- **Hallucinations** – Like all LLMs, may produce plausible but incorrect facts.
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- **Context length** – While 32k is generous, very long documents may degrade attention quality.
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---
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## 🗺️ Roadmap
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- **Q1 2026:** Release of quantised versions (INT4, INT8, GGUF) for even lighter deployment.
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- **Q2 2026:** Neura‑FA‑EN‑3B – 3.5B parameters, expanded Persian vocabulary, improved reasoning.
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- **Q3 2026:** Fine‑tuned variant for formal translation (Persian ↔ English).
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- **Ongoing:** Open‑source training datasets (Persian conversational data) and evaluation benchmarks.
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---
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## 🤝 Contribute
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This model is free and open‑source. You can help by:
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- **Reporting bugs** or suggesting improvements in the [Discussions](https://huggingface.co/neuracoder/neura-fa-en-1.9b/discussions) tab.
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- **Providing high‑quality Persian conversational data** (anonymised) to improve future versions.
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- **Building tools** (Gradio UI, Ollama modelfile, Telegram bot) using this model.
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- **Financial sponsorship** – Contact the Neuracoder team.
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- **Spreading the word** – Every user helps the Persian AI community grow.
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---
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## 📜 License
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**Apache License 2.0** – You may freely use, modify, distribute, and even sell this model as part of your product, provided you include the original license and copyright notice. No other restrictions.
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---
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## 📞 Contact
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- **Website:** [neuracoder.net](https://neuracoder.net) (coming soon)
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- **Email:** info@neuracoder.net
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- **Telegram:** @Neuracoder
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- **GitHub:** [github.com/neura_coder](https://github.com/neura_coder)
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---
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**ساخته شده با ❤️ در ایران – تیم neuracoder**
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*دموکراتیزه کردن هوش مصنوعی مکالمهای برای فارسیزبانان، سریع، محلی و رایگان برای همه.*
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