Instructions to use hsanyyasyn97gmail/cosmos-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hsanyyasyn97gmail/cosmos-v2 with Transformers:

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

pipe = pipeline("text-generation", model="hsanyyasyn97gmail/cosmos-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hsanyyasyn97gmail/cosmos-v2")
model = AutoModelForCausalLM.from_pretrained("hsanyyasyn97gmail/cosmos-v2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hsanyyasyn97gmail/cosmos-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hsanyyasyn97gmail/cosmos-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hsanyyasyn97gmail/cosmos-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hsanyyasyn97gmail/cosmos-v2

SGLang

How to use hsanyyasyn97gmail/cosmos-v2 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 "hsanyyasyn97gmail/cosmos-v2" \
    --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": "hsanyyasyn97gmail/cosmos-v2",
		"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 "hsanyyasyn97gmail/cosmos-v2" \
        --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": "hsanyyasyn97gmail/cosmos-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hsanyyasyn97gmail/cosmos-v2 with Docker Model Runner:
```
docker model run hf.co/hsanyyasyn97gmail/cosmos-v2
```

Model Card for Cosmos-v2

Model Details

Model Description

Cosmos-v2 is a 2.1 billion parameter causal language model trained from scratch. It is based on a Qwen-like architecture and was developed over a period of 3 years through extensive experimentation (more than 116 training runs, of which 113 failed).

The model was trained on 16 billion carefully curated and manually filtered high-quality tokens. It currently shows decent understanding and reasoning capabilities but remains significantly under-trained due to the limited data volume.

Important: This model is still in an experimental stage and is currently undergoing Direct Preference Optimization (DPO) for safety alignment. At the moment, it is largely uncensored and does not reliably refuse harmful requests.

Developed by: hsanyyasyn97
Model type: Causal Language Model (Decoder-only)
Language(s) (NLP): Primarily multilingual with focus on Arabic and English
License: [Specify your license, e.g. Apache 2.0 or other]
Finetuned from model: None (trained from scratch)

Model Sources

Repository: https://huggingface.co/hsanyyasyn97gmail/cosmos-v2

Uses

Direct Use

This model can be used for text generation and basic conversational tasks. It is intended for research and experimentation purposes only.

Downstream Use

Possible future use after proper alignment: further fine-tuning, instruction tuning, or domain adaptation.

Out-of-Scope Use

Any production or high-stakes application
Generating harmful, illegal, or dangerous content
Relying on the model for factual accuracy without verification
Use in safety-critical systems

Bias, Risks, and Limitations

The model is currently uncensored and may generate harmful, dangerous, or unethical content without refusal.
Trained on only 16B tokens, so it is under-trained and has limited knowledge coverage.
May produce biased, incorrect, or nonsensical outputs.
Performance on complex reasoning and long-context tasks is still limited.
Arabic and English performance is better than other languages.

Recommendations

Users should be fully aware that this is an experimental, partially aligned model.
Do not use it for sensitive or production purposes until safety alignment (DPO) is completed.
Always review and filter the model's outputs.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "hsanyyasyn97gmail/cosmos-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data

Total tokens: 16 billion
Data was heavily filtered, cleaned, and manually curated over a long period (high quality, not random web scrapes)
Mix of Arabic and English data + some additional sources

Training Procedure

Trained from scratch (not continued pretraining)
Architecture: Qwen-like
Framework: JAX + XLA
Hardware: Cloud TPU v5 (8 chips)
Current stage: Undergoing DPO (Direct Preference Optimization) using open-source safety preference data (including Anthropic-sourced data) + synthetic preference pairs

Training Hyperparameters

Precision: Mixed precision (details to be updated)
Optimizer and scheduler: Standard for JAX TPU training

Evaluation
No formal public evaluation results available yet.

Internal testing shows reasonable language understanding and basic reasoning, but clear limitations due to limited training tokens.
Environmental Impact

Hardware Type: Google TPU v5
Cloud Provider: Cloud TPU (exact provider not specified)
Hours used: Significant (multiple long training runs over 3 years)
Carbon Emitted: Not calculated

Technical Specifications
Model Architecture and Objective

Architecture: Qwen-like decoder-only Transformer
Parameters: 2.1 Billion
Objective: Causal language modeling (next-token prediction)
Current additional objective: DPO for preference alignment

Compute Infrastructure
Hardware

8× TPU v5
80 vCPU
330 GB RAM

Software

JAX (raw) + XLA
Transformers (for inference and model card)

Model Card Authors
hsanyyasyn97
Model Card Contact
Contact via Hugging Face profile: hsanyyasyn97gmail

Downloads last month: 1,050

Safetensors

Model size

2B params

Tensor type

F32

F16