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Model: Loom-Labs/Apollo-1-2B Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- Qwen/Qwen3-1.7B
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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license: other
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license_name: anvdl-1.0
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license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md
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language:
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- en
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- fr
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- pt
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- de
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- ro
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- sv
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- da
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- bg
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- ru
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- cs
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- el
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- uk
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- es
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- nl
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- sk
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- hr
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- pl
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- lt
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- nb
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- nn
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- fa
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- sl
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- gu
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- lv
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- it
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- oc
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- ne
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- mr
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- be
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- sr
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- lb
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- vec
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- as
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- cy
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- szl
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- ast
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- hne
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- awa
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- mai
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- bho
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- sd
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- ga
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- fo
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- hi
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- pa
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- bn
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- or
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- tg
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- yi
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- lmo
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- lij
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- scn
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- fur
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- sc
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- gl
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- ca
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- is
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- sq
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- li
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- prs
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- af
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- mk
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- si
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- ur
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- mag
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- bs
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- hy
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- zh
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- yue
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- my
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- ar
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- he
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- mt
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- id
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- ms
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- tl
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- ceb
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- jv
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- su
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- min
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- ban
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- pag
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- ilo
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- war
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- ta
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- te
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- kn
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- ml
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- tr
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- az
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- uz
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- kk
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- ba
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- tt
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- th
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- lo
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- fi
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- et
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- hu
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- vi
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- km
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- ja
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- ko
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- ka
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- eu
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- ht
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- pap
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- kea
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- tpi
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- sw
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---
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# Apollo-1-2B
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[](https://huggingface.co/NoemaResearch/Apollo-1-2B)
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[](https://huggingface.co/Qwen/Qwen3-1.7B)
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[](LICENSE)
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Apollo-1-2B is a **2 billion parameter instruction-tuned model** developed by **Noema Research**.
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It is based on [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) and optimized for **general reasoning, language understanding, and lightweight deployment**.
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This model is the first release in the **Apollo series**, intended as a foundation for scalable experimentation and real-world applications in constrained environments.
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---
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## Model Overview
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- **Base model:** `Qwen3-1.7B`
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- **Architecture:** Decoder-only transformer
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- **Parameters:** ~2B
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- **Context length:** up to 32k tokens (inherits Qwen3 long-context support)
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- **Domain:** General-purpose reasoning and instruction following
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- **Primary applications:**
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- Conversational AI
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- Lightweight reasoning tasks
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- Education and tutoring
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- Prototype agents and assistants
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- **License:** anvdl-1.0
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---
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## Key Features
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- **Instruction tuned**: More reliable responses in conversational and task-oriented settings
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- **Lightweight deployment**: Optimized for environments with limited compute or memory resources
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- **Extended context**: Inherits long-context capability from Qwen3 base
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- **Balanced outputs**: Improved refusal behaviors and reduced hallucinations compared to the base model
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- **Multilingual ability**: Retains multilingual knowledge from Qwen3 family
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---
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## Usage
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The model is available in Hugging Face Transformers format. Example:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "NoemaResearch/Apollo-1-2B"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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messages = [
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{"role":"system", "content":"You are Apollo, a reasoning assistant."},
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{"role":"user", "content":"Explain the difference between supervised and unsupervised learning."}
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]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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````
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**Recommended settings:**
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* `temperature=0.5–0.9`
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* `top_p=0.85–0.95`
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* For structured outputs (e.g. JSON), use lower temperatures for stability
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---
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## Evaluation
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Apollo-1-2B has been evaluated internally on a range of reasoning and language tasks. Key findings:
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* Improved **instruction following** relative to Qwen3-1.7B
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* More **concise and accurate responses** in structured tasks
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* Maintains **multilingual performance** from the base model
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* Effective for **lightweight assistant applications**
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Future work will include publishing comprehensive benchmark comparisons against other models in the 1–3B parameter range.
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---
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## Limitations
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* **Reasoning depth**: As a 2B parameter model, Apollo cannot match larger-scale LLMs on complex reasoning tasks
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* **Knowledge coverage**: May lack depth in specialized or low-resource domains
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* **Hallucinations**: Although reduced, the model may still generate incorrect or fabricated information
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* **Sensitivity to prompts**: Outputs vary with prompt phrasing; careful prompt design recommended
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---
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## Responsible Use
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* Do not rely on Apollo for critical decision-making without human oversight
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* Generated outputs may contain inaccuracies; verification is required for factual or sensitive use cases
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* Avoid providing personal, private, or sensitive information in prompts
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* This model should not be used to generate disallowed, unsafe, or harmful content
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---
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## Model Variants
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* **Full precision (safetensors)** — research and full-fidelity inference
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* **bf16 / fp16** — optimized for inference on GPUs/TPUs
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* **Quantized versions (int8 / int4)** — for deployment in constrained hardware environments
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---
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## Citation
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If you use this model, please cite both Apollo-1-2B and the Qwen3 base model:
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```bibtex
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@misc{noema2025apollo,
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title={Apollo-1-2B},
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author={Noema Research},
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year={2025},
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howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-2B}}
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}
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```
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---
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## Acknowledgements
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Apollo-1-2B builds upon the [Qwen3](https://huggingface.co/Qwen) series of models.
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We thank the Qwen team for making their work openly available under permissive terms, which enabled this derivative research.
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---
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