Instructions to use rita-cohere/tya-eng-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rita-cohere/tya-eng-v1 with Transformers:

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

pipe = pipeline("text-generation", model="rita-cohere/tya-eng-v1")
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("rita-cohere/tya-eng-v1")
model = AutoModelForCausalLM.from_pretrained("rita-cohere/tya-eng-v1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rita-cohere/tya-eng-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rita-cohere/tya-eng-v1

SGLang

How to use rita-cohere/tya-eng-v1 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 "rita-cohere/tya-eng-v1" \
    --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": "rita-cohere/tya-eng-v1",
		"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 "rita-cohere/tya-eng-v1" \
        --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": "rita-cohere/tya-eng-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rita-cohere/tya-eng-v1 with Docker Model Runner:
```
docker model run hf.co/rita-cohere/tya-eng-v1
```

Model Card for tiny-aya-english-reasoning

Model Summary

Private staging upload of Tiny Aya English reasoning SFT (sft-tiny-aya-english-4-1-reasoning), from Ananya Sahu's post-training run (20260618_200856_run), bf16 HF export.

Developed by: Cohere and Cohere Labs

Point of Contact: Cohere Labs
License:CC-BY-NC, requires also adhering to Cohere Lab's Acceptable Use Policy
Model: Tiny Aya English reasoning SFT
Model Size:
Context length:

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Usage

Model Details

Input:

Output:

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Model Card Contact

For errors or additional questions about details in this model card, contact labs@cohere.com

Terms of Use:

We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 111 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC, requires also adhering to Cohere Lab's Acceptable Use Policy If you are interested in commercial use, please contact Cohere's Sales team.

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Safetensors

Model size

3B params

Tensor type

BF16