PaletLabs/Circe

Circe-1.5B is a single-checkpoint, 1.5 B-parameter language model that asks a simple question:

“How far can you push tiny models on a tiny budget?”

⚙️ Spec	Value
Base model	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Trainable params	4 M (LoRA)
Post-training cost	≈ US $12 on 1×L40S
Training recipe	8 h SFT → 4 h GRPO
Context length	up to 4 k tokens (tested)
RAM @ bf16	~9 GB (≤ 3 GB 4-bit GPTQ)
Throughput	~55 tok / s on 1×A6000 (fp16, no compile)

It keeps DeepSeek-R1’s strong reasoning depth but adds fluent bilingual chat (English & Spanish) in a checkpoint that fits on a laptop GPU.
We intend to use it as a reproducible waypoint on the road to real-time speech-to-speech reasoning systems.

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🔭 Intended Use

• Base for new LoRAs — domain adaptation, longer-context studies.
• Research into cost-efficient RL for reasoning.
• Not for high-stakes or production tasks.

See the ⚙️ Limitations section before use.

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⚡ Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("PaletLabs/Circe-1.5B", torch_dtype="bfloat16")
tok   = AutoTokenizer.from_pretrained("PaletLabs/Circe-1.5B")

prompt = "<|user|>¿Cómo se dice “tiny model” en español?<|assistant|>"
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))

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🛠️ Installation

git clone https://github.com/palet-global/circe
cd circe
python -m venv venv && source venv/bin/activate
pip install .

🏗️ Re-Training Pipeline

Data

python data/fetch_datasets.py --out data/processed

Supervised LoRA

accelerate config default            # one-time
accelerate launch train/sft.py \
  --data_dir data/processed \
  --output_dir checkpoints/sft

RL (GRPO)

accelerate launch train/rl_grpo.py \
  --data_dir data/processed \
  --output_dir checkpoints/grpo \
  --init_ckpt checkpoints/sft/checkpoint-13000 \
  --num_steps 3000 --save_steps 500 --group 4

Merge and Tokenizer

python train/merge_lora.py \
  --ckpt_dir checkpoints/grpo \
  --base deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

SQuAD Sanity Checks

python eval/quick_squad_eval.py --model ./merged --dataset squad
python eval/quick_squad_eval.py --model ./merged --dataset squad_es

Upload

python train/upload_to_hub.py \
  --model_dir merged \
  --repo PaletLabs/Circe-1.5B \
  --token $HF_TOKEN

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💻 Hardware & Inference Tips

• bf16 / fp16: Needs ~9 GB VRAM.
• 4-bit GPTQ: < 3 GB. bitsandbytes works out-of-the-box.
• Compile once (torch.compile) for +10–15 % throughput.

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✍️ Current Evaluation Status

Formal lighteval / MMLU / GSM-8K runs are queued. Preliminary spot-checks show Circe retains DeepSeek-R1’s chain-of-thought depth on reasoning-heavy QA while adding smooth bilingual generation.

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⚙️ Limitations & Bias

• No reward-model alignment.
• Long-context (> 4 k) stability untested.
• Training data bias from public QA pairs. Spanish coverage favors Latin American variants.
• Minimal safety filters so you have to wrap with your own guardrails for production.

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🔮 Roadmap

• Publish full reasoning benchmark suite & eval scripts.
• Release code-reasoning and doc-QA adapters.
• Attach a 24 kHz neural codec → real-time, full-duplex voice chat without ASR → TTS hops.

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🪪 License

This project is licensed under the MIT License. Attribution appreciated but not required.