NoemaResearch
/

Apollo-1-2B

@@ -122,131 +122,139 @@ language:
   - sw
 ---
-![Header](./Nous-V1-Banner.png)
-# Nous-V1 8B
-## Overview
-**Nous-V1 2B** is a cutting-edge 8 billion parameter language model developed by Apexion AI, based on the architecture of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). Designed for versatility across diverse NLP tasks, Nous-V1 4B delivers strong performance in conversational AI, knowledge reasoning, code generation, and content creation.
-**Key Features:**
-- **⚡ Efficient 2B Parameter Scale:** Balances model capability with practical deployment on modern hardware
-- **🧠 Enhanced Contextual Understanding:** Supports an 128k token context window, enabling complex multi-turn conversations and document analysis
-- **🌐 Multilingual & Multi-domain:** Trained on a diverse dataset for broad language and domain coverage
-- **🤖 Instruction-Following & Adaptability:** Fine-tuned to respond accurately and adaptively across tasks
-- **🚀 Optimized Inference:** Suitable for GPU environments such as NVIDIA A100, T4, and P100 for low-latency applications
 ---
-## Why Choose Nous-V1 2B?
-While larger models can offer more raw power, Nous-V1 2B strikes a practical balance — optimized for deployment efficiency without significant compromise on language understanding or generation quality. It’s ideal for applications requiring:
-- Real-time conversational agents
-- Code completion and programming assistance
-- Content generation and summarization
-- Multilingual natural language understanding
 ---
-## 🖥️ How to Run Locally
-You can easily integrate Nous-V1 2B via the Hugging Face Transformers library or deploy it on popular serving platforms.
-### Using Hugging Face Transformers
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "apexion-ai/Nous-1-2B"
-# load the tokenizer and the model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
 )
-# prepare the model input
-prompt = "Give me a short introduction to large language model."
 messages = [
-    {"role": "user", "content": prompt}
 ]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-# conduct text completion
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=32768
-)
-output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
-# parsing thinking content
-try:
-    # rindex finding 151668 (</think>)
-    index = len(output_ids) - output_ids[::-1].index(151668)
-except ValueError:
-    index = 0
-thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
-content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
-print("thinking content:", thinking_content)
-print("content:", content)
-```
-### Deployment Options
-- Compatible with [vLLM](https://github.com/vllm-project/vllm) for efficient serving
-- Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) for lightweight inference
 ---
-## Recommended Sampling Parameters
-```yaml
-Temperature: 0.7
-Top-p: 0.9
-Top-k: 40
-Min-p: 0.0
-```
 ---
-## FAQ
-- **Q:** Can I fine-tune Nous-V1 2B on my custom data?
-  **A:** Yes, the model supports fine-tuning workflows via Hugging Face Trainer or custom scripts.
-- **Q:** What hardware is recommended?
-  **A:** NVIDIA GPUs with at least 16GB VRAM (e.g., A100, 3090) are optimal for inference and fine-tuning.
-- **Q:** Is the model safe to use for production?
-  **A:** Nous-V1 2B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content.
 ---
-## 📄 Citation
 ```bibtex
-@misc{apexion2025nousv14b,
-  title={Nous-V1 2B: Efficient Large Language Model for Versatile NLP Applications},
-  author={Apexion AI Team},
   year={2025},
-  url={https://huggingface.co/apexion-ai/Nous-V1-2B}
 }
 ```
 ---
-*Nous-V1 2B — Powering practical AI applications with intelligent language understanding.*

   - sw
 ---
+# Apollo-1-2B
+[![Model](https://img.shields.io/badge/Model-Apollo--1--2B-blue)](https://huggingface.co/NoemaResearch/Apollo-1-2B)
+[![Base](https://img.shields.io/badge/Base-Qwen3--1.7B-green)](https://huggingface.co/Qwen/Qwen3-1.7B)
+[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](LICENSE)
+Apollo-1-2B is a **2 billion parameter instruction-tuned model** developed by **Noema Research**.
+It is based on [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) and optimized for **general reasoning, language understanding, and lightweight deployment**.
+This model is the first release in the **Apollo series**, intended as a foundation for scalable experimentation and real-world applications in constrained environments.
 ---
+## Model Overview
+- **Base model:** `Qwen3-1.7B`
+- **Architecture:** Decoder-only transformer
+- **Parameters:** ~2B
+- **Context length:** up to 32k tokens (inherits Qwen3 long-context support)
+- **Domain:** General-purpose reasoning and instruction following
+- **Primary applications:**
+  - Conversational AI
+  - Lightweight reasoning tasks
+  - Education and tutoring
+  - Prototype agents and assistants
+- **License:** anvdl-1.0
 ---
+## Key Features
+- **Instruction tuned**: More reliable responses in conversational and task-oriented settings
+- **Lightweight deployment**: Optimized for environments with limited compute or memory resources
+- **Extended context**: Inherits long-context capability from Qwen3 base
+- **Balanced outputs**: Improved refusal behaviors and reduced hallucinations compared to the base model
+- **Multilingual ability**: Retains multilingual knowledge from Qwen3 family
+---
+## Usage
+The model is available in Hugging Face Transformers format. Example:
 ```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "NoemaResearch/Apollo-1-2B"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
 )
 messages = [
+    {"role":"system", "content":"You are Apollo, a reasoning assistant."},
+    {"role":"user", "content":"Explain the difference between supervised and unsupervised learning."}
 ]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+````
+**Recommended settings:**
+* `temperature=0.5–0.9`
+* `top_p=0.85–0.95`
+* For structured outputs (e.g. JSON), use lower temperatures for stability
+---
+## Evaluation
+Apollo-1-2B has been evaluated internally on a range of reasoning and language tasks. Key findings:
+* Improved **instruction following** relative to Qwen3-1.7B
+* More **concise and accurate responses** in structured tasks
+* Maintains **multilingual performance** from the base model
+* Effective for **lightweight assistant applications**
+Future work will include publishing comprehensive benchmark comparisons against other models in the 1–3B parameter range.
 ---
+## Limitations
+* **Reasoning depth**: As a 2B parameter model, Apollo cannot match larger-scale LLMs on complex reasoning tasks
+* **Knowledge coverage**: May lack depth in specialized or low-resource domains
+* **Hallucinations**: Although reduced, the model may still generate incorrect or fabricated information
+* **Sensitivity to prompts**: Outputs vary with prompt phrasing; careful prompt design recommended
 ---
+## Responsible Use
+* Do not rely on Apollo for critical decision-making without human oversight
+* Generated outputs may contain inaccuracies; verification is required for factual or sensitive use cases
+* Avoid providing personal, private, or sensitive information in prompts
+* This model should not be used to generate disallowed, unsafe, or harmful content
+---
+## Model Variants
+* **Full precision (safetensors)** — research and full-fidelity inference
+* **bf16 / fp16** — optimized for inference on GPUs/TPUs
+* **Quantized versions (int8 / int4)** — for deployment in constrained hardware environments
 ---
+## Citation
+If you use this model, please cite both Apollo-1-2B and the Qwen3 base model:
 ```bibtex
+@misc{noema2025apollo,
+  title={Apollo-1-2B},
+  author={Noema Research},
   year={2025},
+  howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-2B}}
 }
 ```
 ---
+## Acknowledgements
+Apollo-1-2B builds upon the [Qwen3](https://huggingface.co/Qwen) series of models.
+We thank the Qwen team for making their work openly available under permissive terms, which enabled this derivative research.
+---