language, license, base_model, library_name, pipeline_tag, tags

language	license	base_model	library_name	pipeline_tag	tags
en	other	Qwen/Qwen2.5-1.5B-Instruct	transformers	text-generation	crca causal-reasoning qwen2 1.5b finetuned

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.