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Model: neuracoder/neura-fa-en-1.9b Source: Original Platform
2026-07-17 02:18:09 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- fa
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- conversational
+- text-generation
+- persian
+- farsi
+- bilingual
+- lightweight
+- cpu
+- iranian-company
+- neuracoder
+- research-model
+pipeline_tag: text-generation
+datasets:
+- parsinlp/fa-chat
+- openchat_sharegpt4_fa
+metrics:
+- perplexity
+- rouge
+- bleu
+---
+
+# 🗣️ Neura-FA-EN-1.9B
+
+**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**.
+
+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.
+
+---
+![neura-fa-en-1.9b](https://huggingface.co/neuracoder/neura-fa-en-1.9b/resolve/main/neura-fa-en-1.9b.png)
+
+## ✨ Key Features
+
+- **Truly bilingual (Persian + English)** – Understands and generates both languages fluently, with natural code‑switching (e.g., “بگو hello world به انگلیسی”).
+- **Ultra‑lightweight** – Only 1.9B parameters, ~1.6 GB (FP16) / ~0.9 GB (INT8). Runs on 4 GB RAM devices.
+- **Offline & private** – No internet connection or API key needed after download.
+- **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.
+- **Long context** – 32,768 tokens (≈24,000 Persian words), enough for long conversations or short stories.
+- **Iranian‑made, Apache 2.0** – Free for commercial and personal use, with full transparency.
+- **Research‑friendly** – Released as a **research model** to help the Persian AI community fine‑tune, quantise, or build upon it.
+
+---
+
+## 🎯 Suitable Use Cases
+
+- **Daily chit‑chat** – Casual conversation, small talk, jokes, and friendly assistant tasks.
+- **Simple Q&A** – Answering general knowledge questions (e.g., “پایتخت فرانسه کجاست?” / “What is the capital of France?”).
+- **Informal translation** – Translating short sentences or phrases between Persian and English (not professional/legal grade).
+- **Light summarisation** – Summarising a paragraph or a short article in Persian or English.
+- **Brainstorming & writing help** – Generating ideas, rewriting a sentence, fixing simple grammar.
+- **Educational tool for language learning** – Practicing Persian or English conversations (basic to intermediate level).
+- **Offline assistant for edge devices** – Embedded in chatbots, local web UIs, or Telegram bots (simple integration).
+
+### ❌ Not suitable for:
+- Code generation, debugging, or programming assistance.
+- Complex mathematical reasoning or multi‑step logic.
+- Professional translation (e.g., legal, medical).
+- Long document processing (>32k tokens).
+- Any task requiring up‑to‑date information after mid‑2024.
+
+---
+
+## 📊 Evaluation & Performance Metrics
+
+We evaluated Neura‑FA‑EN‑1.9B on standard Persian and English benchmarks for conversational models.
+
+| Dataset              | Metric       | Score   | Note                                      |
+|----------------------|--------------|---------|-------------------------------------------|
+| ParsiMMLU (5‑shot)   | Accuracy     | 48.7%   | General knowledge in Persian              |
+| PersianQA            | Exact Match  | 56.2%   | Reading comprehension (questions in Persian) |
+| MMLU (English, 5‑shot)| Accuracy    | 51.3%   | General knowledge in English              |
+| XNLI (fa)            | Accuracy     | 62.1%   | Natural language inference (Persian)      |
+| XNLI (en)            | Accuracy     | 68.5%   | Natural language inference (English)      |
+| Perplexity (fa‑wikitext)| PPL        | 18.3    | Fluency on Persian texts                  |
+
+> **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.
+
+---
+
+## 📈 Comparison with Similar‑Sized Models
+
+| Model                     | Params | Persian MMLU | English MMLU | VRAM (FP16) | Speed (tok/s, T4) | License    |
+|---------------------------|--------|--------------|--------------|-------------|-------------------|------------|
+| **Neura-FA-EN-1.9B**      | 1.9B   | **48.7%**    | 51.3%        | ~3.8 GB     | 48                | Apache 2.0 |
+| Arian‑2B (Persian)        | 2.0B   | 44.2%        | 28.7%        | ~4.0 GB     | 45                | Apache 2.0 |
+| Phi‑2 (2.7B, English‑only)| 2.7B   | N/A          | 57.8%        | ~5.4 GB     | 40                | MIT        |
+| Gemma‑2B (English‑only)   | 2.0B   | N/A          | 52.6%        | ~4.0 GB     | 52                | Gemma      |
+
+> **Key points:** Neura‑FA‑EN is the **only 1.9B model** that provides strong performance on **both** Persian and English.
+
+---
+
+## 🧪 Technical Details & Training Process
+
+Built on the **Qwen2 architecture** (only the architecture, not derived from any existing model) and trained from scratch by Neuracoder.
+
+### Architecture
+- **Layers:** 28 decoder‑only layers.
+- **Attention:** Grouped Query Attention (GQA) – 12 query heads, 2 key/value heads.
+- **Activation:** SwiGLU.
+- **Context length:** 32,768 tokens.
+- **Embedding size:** 2048.
+- **Intermediate size:** 5632.
+
+### Pre‑training
+- **Data:** 350 billion tokens – 60% Persian (web texts, books, news, forums), 35% English (common crawl, books, Wikipedia), 5% code (to preserve basic formatting).
+- **Duration:** 18 days on 8× NVIDIA A100 (80GB) using DeepSpeed ZeRO‑3.
+- **Hyperparameters:** AdamW (lr=3e-4), cosine decay, warmup 2000 steps, batch size 512, seq len 2048 (later extended to 8192 with RoPE scaling).
+
+### Supervised Fine‑Tuning (SFT)
+- **Data:** 150,000 conversation pairs in Persian and English:
+  - 80,000 from public Persian chat datasets (ParsiNLU, FaChat).
+  - 50,000 from translated and cleaned ShareGPT data.
+  - 20,000 hand‑written by Neuracoder team for natural code‑switching and cultural relevance.
+- **Format:** `{"system": "You are a helpful assistant.", "user": "...", "assistant": "..."}`
+- **Hyperparameters:** 3 epochs, lr=1e-5, batch size 128, LoRA (rank=32) then full fine‑tune last 6 layers.
+
+### Validation
+- Every 500 steps evaluated on held‑out Persian and English test sets.
+- Final checkpoint chosen by lowest perplexity on Persian validation and highest MMLU score.
+
+---
+
+## ⚡ Inference Speed & Hardware Requirements
+
+| Hardware                 | Weight format | Avg tokens/sec (gen 256 tokens) | Memory usage |
+|--------------------------|---------------|----------------------------------|---------------|
+| NVIDIA A100 (40GB)       | FP16          | 78 tok/s                         | 4.1 GB        |
+| NVIDIA T4 (16GB)         | FP16          | 48 tok/s                         | 3.9 GB        |
+| NVIDIA T4 (16GB)         | INT8          | 55 tok/s                         | 2.3 GB        |
+| NVIDIA GTX 1060 (6GB)    | FP16          | 28 tok/s                         | 3.9 GB        |
+| CPU (Intel i7-12700K)    | INT8          | 9 tok/s                          | 2.1 GB        |
+| Raspberry Pi 4 (4GB)     | INT8 (ONNX)   | 2–3 tok/s                        | 1.6 GB        |
+
+> **Recommendation:** Use FP16 on any GPU with 6+ GB VRAM. For CPU or low‑memory devices, use INT8 quantised version (available separately).
+
+---
+
+## 🚀 Usage Guide
+
+### Installation
+
+```
+pip install transformers torch accelerate sentencepiece
+```
+
+### Example 1: Basic Persian conversation
+
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "neuracoder/neura-fa-en-1.9b"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+prompt = "به نظرت بهترین راه برای یادگیری زبان انگلیسی چیه؟"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Example 2: Mixed Persian‑English query
+
+```
+prompt = "یه جمله انگلیسی بنویس که معنی 'خورشید می‌تابد' رو برسونه"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.6)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+### Example 3: Simple summarisation (English)
+
+```
+article = """The Persian cat is a long-haired breed characterized by its round face and short muzzle. 
+It is one of the oldest cat breeds, originating from Persia (modern-day Iran)."""
+prompt = f"Summarise the following text in one sentence:\n\n{article}"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=80, temperature=0.3)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+### Download the model directly
+
+```
+git lfs install
+git clone https://huggingface.co/neuracoder/neura-fa-en-1.9b
+```
+
+Or via Python:
+
+```
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="neuracoder/neura-fa-en-1.9b", local_dir="./neura-fa-en-1.9b")
+```
+
+---
+
+## ⚠️ Limitations
+
+- **Not a code model** – Cannot write or debug programs reliably.
+- **Not a mathematical engine** – Struggles with multi‑step arithmetic or symbolic reasoning.
+- **Knowledge cutoff** – Mid‑2024. Unaware of very recent events or new APIs.
+- **Persian dialect** – Trained on standard Persian (Farsi); may not understand Dari or Tajik well.
+- **Formal translation** – Not suitable for legal, medical, or highly technical documents.
+- **Hallucinations** – Like all LLMs, may produce plausible but incorrect facts.
+- **Context length** – While 32k is generous, very long documents may degrade attention quality.
+
+---
+
+## 🗺️ Roadmap
+
+- **Q1 2026:** Release of quantised versions (INT4, INT8, GGUF) for even lighter deployment.
+- **Q2 2026:** Neura‑FA‑EN‑3B – 3.5B parameters, expanded Persian vocabulary, improved reasoning.
+- **Q3 2026:** Fine‑tuned variant for formal translation (Persian ↔ English).
+- **Ongoing:** Open‑source training datasets (Persian conversational data) and evaluation benchmarks.
+
+---
+
+## 🤝 Contribute
+
+This model is free and open‑source. You can help by:
+
+- **Reporting bugs** or suggesting improvements in the [Discussions](https://huggingface.co/neuracoder/neura-fa-en-1.9b/discussions) tab.
+- **Providing high‑quality Persian conversational data** (anonymised) to improve future versions.
+- **Building tools** (Gradio UI, Ollama modelfile, Telegram bot) using this model.
+- **Financial sponsorship** – Contact the Neuracoder team.
+- **Spreading the word** – Every user helps the Persian AI community grow.
+
+---
+
+## 📜 License
+
+**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.
+
+---
+
+## 📞 Contact
+
+- **Website:** [neuracoder.net](https://neuracoder.net) (coming soon)
+- **Email:** info@neuracoder.net
+- **Telegram:** @Neuracoder
+- **GitHub:** [github.com/neura_coder](https://github.com/neura_coder)
+
+---
+
+**ساخته شده با ❤️ در ایران – تیم neuracoder**  
+*دموکراتیزه کردن هوش مصنوعی مکالمه‌ای برای فارسی‌زبانان، سریع، محلی و رایگان برای همه.*
--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Neura, an advanced and helpful AI. You speak both Persian and English fluently and respond based on user needs.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Neura, an advanced and helpful AI. You speak both Persian and English fluently and respond based on user needs.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,61 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.10.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.10.2"
+}
--- a/model.safetensors
+++ b/model.safetensors
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+size 3087466808
--- a/neura-fa-en-1.9b.png
+++ b/neura-fa-en-1.9b.png
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--- a/tokenizer.json
+++ b/tokenizer.json
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--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}