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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Euroswarms/CR-CA")
model = AutoModelForCausalLM.from_pretrained("Euroswarms/CR-CA", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

EuroswarmsInstitute commited on Jan 30

Commit

cdcafaf

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README.md +54 -3

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@@ -1,3 +1,54 @@
----
-license: eupl-1.2
----

+---
+language:
+  - en
+license: other
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - crca
+  - causal-reasoning
+  - qwen2
+  - 1.5b
+  - finetuned
+---
+# CRCA 1.5B Full Finetune
+## Overview
+CRCA reasoning-optimized 1.5B causal language model, based on the Qwen2 architecture
+(`Qwen2ForCausalLM`). This model is intended for causal reasoning, counterfactual
+analysis, and CRCA-style structured tasks.
+## 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 CRCA reasoning. Training metadata
+is stored in `training_args.bin`.
+## Intended Use
+For causal reasoning, counterfactual analysis, and structured CRCA reasoning prompts.
+## 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.