Instructions to use Euroswarms/CR-CA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Euroswarms/CR-CA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Euroswarms/CR-CA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Euroswarms/CR-CA")
model = AutoModelForCausalLM.from_pretrained("Euroswarms/CR-CA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Euroswarms/CR-CA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Euroswarms/CR-CA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Euroswarms/CR-CA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Euroswarms/CR-CA

SGLang

How to use Euroswarms/CR-CA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Euroswarms/CR-CA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Euroswarms/CR-CA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Euroswarms/CR-CA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Euroswarms/CR-CA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Euroswarms/CR-CA with Docker Model Runner:
```
docker model run hf.co/Euroswarms/CR-CA
```

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.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Euroswarms/CR-CA

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

(1553)

this model

Finetunes

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