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Euroswarms/CR-CA

sourceHugging Faceotherupdated 8mo agoView on Hugging Face
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CRCA 1.5B Full Finetune

Overview

CR-CA (Causal Reasoning and Counterfactual Analysis) is a reasoning-focused stack that targets structured causal analysis, counterfactuals, and multi-step reasoning. This 1.5B model is a CR-CA reasoning-optimized causal language model based on the Qwen2 architecture (Qwen2ForCausalLM).

Model Details

  • —Model type: qwen2
  • —Architecture: Qwen2ForCausalLM
  • —Hidden size: 1536
  • —Layers: 28
  • —Attention heads: 12 (KV heads: 2)
  • —Max position embeddings: 32768
  • —Vocab size: 151936
  • —Dtype: float16

Training Summary

This model was produced via full finetuning for CR-CA reasoning. Training metadata is stored in training_args.bin.

Key training parameters:

  • —Per-device batch size: 8
  • —Gradient accumulation: 16
  • —Epochs: 2
  • —Learning rate: 5e-4
  • —Precision: FP16
  • —DeepSpeed config: training/deepspeed_zero2_1_5b.json
  • —Scheduler: cosine
  • —Warmup steps: 100
  • —Save steps: 200

Training Data

The training data uses a prompt/response JSONL format:

{"prompt": "...", "response": "..."}

The dataset includes public reasoning data (e.g., GSM8K-style math word problems). This is used to strengthen multi-step reasoning, structured derivations, and final answer formatting.

Evaluation Report (Real-World Causal Tasks)

Evaluation was run on 2026-02-01 using GPT-4o-mini over 6 real-world causal tasks. Overall score: 48.3%.

Per-task scores:

  • —Monetary Policy Counterfactual (US Macro 2025): 55/100
  • —Tariff Pass-Through and Pricing (Beige Book + Firm Data): 55/100
  • —Supply Chain Reroute Counterfactual (Port Disruption): 45/100
  • —Inventory & Stockout Causal Impact (Retail): 25/100
  • —Inflation Drivers (World Bank CPI Data): 65/100
  • —Workforce Training Program (Labor Market Causal Impact): 45/100

Key strengths observed:

  • —Clear task framing and attempt at counterfactual reasoning.
  • —Some identification of confounders and causal factors.

Key limitations observed:

  • —Inconsistent causal graphs and directional effects.
  • —Weak counterfactual grounding and numerical reasoning errors.
  • —Limited depth and rigor on confounder adjustment strategies.

Intended Use

For causal reasoning, counterfactual analysis, structured CR-CA reasoning prompts, and multi-step reasoning tasks.

Generation Settings

Default generation parameters are stored in generation_config.json:

  • —do_sample: true
  • —temperature: 0.7
  • —top_p: 0.8
  • —top_k: 20
  • —repetition_penalty: 1.1

Limitations

  • —Outputs should be validated for factual correctness.
  • —The model may hallucinate causal claims without evidence.

License

Follow the base model and dataset licenses used for training. Add your explicit license here if required.