100 lines
2.9 KiB
Markdown
100 lines
2.9 KiB
Markdown
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---
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language:
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- en
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license: other
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- crca
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- causal-reasoning
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- qwen2
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- 1.5b
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- finetuned
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---
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# CRCA 1.5B Full Finetune
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## Overview
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CR-CA (Causal Reasoning and Counterfactual Analysis) is a reasoning-focused stack
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that targets structured causal analysis, counterfactuals, and multi-step reasoning.
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This 1.5B model is a CR-CA reasoning-optimized causal language model based on the
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Qwen2 architecture (`Qwen2ForCausalLM`).
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## Model Details
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- **Model type:** `qwen2`
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- **Architecture:** `Qwen2ForCausalLM`
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- **Hidden size:** `1536`
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- **Layers:** `28`
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- **Attention heads:** `12` (KV heads: `2`)
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- **Max position embeddings:** `32768`
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- **Vocab size:** `151936`
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- **Dtype:** `float16`
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## Training Summary
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This model was produced via full finetuning for CR-CA reasoning. Training metadata
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is stored in `training_args.bin`.
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Key training parameters:
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- **Per-device batch size:** 8
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- **Gradient accumulation:** 16
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- **Epochs:** 2
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- **Learning rate:** 5e-4
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- **Precision:** FP16
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- **DeepSpeed config:** `training/deepspeed_zero2_1_5b.json`
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- **Scheduler:** cosine
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- **Warmup steps:** 100
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- **Save steps:** 200
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## Training Data
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The training data uses a prompt/response JSONL format:
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```
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{"prompt": "...", "response": "..."}
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```
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The dataset includes public reasoning data (e.g., GSM8K-style math word problems).
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This is used to strengthen multi-step reasoning, structured derivations, and final
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answer formatting.
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## Evaluation Report (Real-World Causal Tasks)
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Evaluation was run on 2026-02-01 using GPT-4o-mini over 6 real-world causal tasks.
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Overall score: **48.3%**.
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Per-task scores:
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- Monetary Policy Counterfactual (US Macro 2025): **55/100**
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- Tariff Pass-Through and Pricing (Beige Book + Firm Data): **55/100**
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- Supply Chain Reroute Counterfactual (Port Disruption): **45/100**
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- Inventory & Stockout Causal Impact (Retail): **25/100**
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- Inflation Drivers (World Bank CPI Data): **65/100**
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- Workforce Training Program (Labor Market Causal Impact): **45/100**
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Key strengths observed:
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- Clear task framing and attempt at counterfactual reasoning.
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- Some identification of confounders and causal factors.
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Key limitations observed:
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- Inconsistent causal graphs and directional effects.
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- Weak counterfactual grounding and numerical reasoning errors.
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- Limited depth and rigor on confounder adjustment strategies.
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## Intended Use
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For causal reasoning, counterfactual analysis, structured CR-CA reasoning prompts,
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and multi-step reasoning tasks.
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## Generation Settings
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Default generation parameters are stored in `generation_config.json`:
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- `do_sample`: `true`
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- `temperature`: `0.7`
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- `top_p`: `0.8`
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- `top_k`: `20`
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- `repetition_penalty`: `1.1`
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## Limitations
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- Outputs should be validated for factual correctness.
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- The model may hallucinate causal claims without evidence.
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## License
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Follow the base model and dataset licenses used for training. Add your explicit
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license here if required.
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