Instructions to use redptam/tyrian-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redptam/tyrian-1b with Transformers:

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

pipe = pipeline("text-generation", model="redptam/tyrian-1b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("redptam/tyrian-1b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use redptam/tyrian-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redptam/tyrian-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redptam/tyrian-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/redptam/tyrian-1b

SGLang

How to use redptam/tyrian-1b 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 "redptam/tyrian-1b" \
    --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": "redptam/tyrian-1b",
		"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 "redptam/tyrian-1b" \
        --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": "redptam/tyrian-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use redptam/tyrian-1b with Docker Model Runner:
```
docker model run hf.co/redptam/tyrian-1b
```

Tyrian 1B

A 988M parameter decoder-only language model built from scratch in PyTorch, then supervised fine-tuned for chat (ChatML) and preference-tuned with DPO. Every component (tokenizer, architecture, data pipeline, training loop, SFT, DPO) was written from scratch with Claude (Anthropic's AI assistant).

Model Details

Property	Value
Parameters	988,350,464
Hidden size	2048
Layers	16
Query / KV heads	32 / 8 (GQA)
FFN size	7680 (SwiGLU)
Context length	8192 tokens
Vocab size	32,000
Position encoding	RoPE (θ=500000)
Normalization	RMSNorm (pre-norm)
Biases	None
Embeddings	Tied

Training

Pretraining — 25B tokens, seq_len 8192, 512K tokens/step, cosine LR 3e-4 → 3e-5 with 200-step warmup, AdamW (β₁=0.9, β₂=0.95), weight decay 0.1, 2× RTX 5060 Ti 16GB (DDP). Data mix: FineWeb-Edu, Cosmopedia, StackExchange, Wikipedia, OpenWebText, WildChat, LMSYS-Chat, UltraChat, OASST2, UltraFeedback, CodeSearchNet (Python).

SFT — 100K examples from OpenHermes-2.5, ChatML with loss masked on non-assistant tokens, 3 epochs, LR 2e-5 → 2e-6 (SFT step 1023).

DPO — 1 epoch over 62,785 preference pairs: 51,989 from UltraFeedback (tied scores dropped) and 10,796 from PKU-SafeRLHF (pairs where exactly one response is labelled safe; that one is preferred). β=0.1, LR 1e-6 → 1e-7 with 10% warmup, 64 pairs/step, frozen SFT model as reference (DPO step 981). Held-out pair accuracy rose from 0.51 to 0.71 (0.79 on the safety pairs); held-out perplexity rose by about 1%.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("redptam/tyrian-1b", trust_remote_code=True,
                                             dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained("redptam/tyrian-1b", trust_remote_code=True)

prompt = tokenizer.apply_chat_template([{"role": "user", "content": "What is the capital of France?"}],
                                       tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
im_end = tokenizer.convert_tokens_to_ids("<|im_end|>")
output = model.generate(inputs["input_ids"], max_new_tokens=100, temperature=0.8, top_k=50,
                        stop_token_ids=(im_end,))
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Assistant turns end with <|im_end|> (id 5); use it as the stop token.

Serving with tyrian-serve

tyrian-serve is an OpenAI-compatible server for the Tyrian models (/v1/chat/completions, with streaming), so it can be used from Open WebUI or any OpenAI client. It runs on CUDA and falls back to CPU; on an RTX 5060 Ti this model generates about 110 tokens/s.

git clone https://github.com/redptam/tyrian-serve && cd tyrian-serve
python3 -m venv .venv
./.venv/bin/pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu130
./.venv/bin/hf download redptam/tyrian-1b --local-dir .modelcache/tyrian-1b
MODEL_PATH=.modelcache/tyrian-1b MODEL_NAME=tyrian-1b ./.venv/bin/python -m uvicorn server:app --host 0.0.0.0 --port 8000

curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"model":"tyrian-1b","messages":[{"role":"user","content":"What is the capital of France?"}],"max_tokens":1200,"repetition_penalty":1.15}'

Use a repetition_penalty of about 1.15 (tyrian-serve's default). Without one, about one reply in eight loops until it hits the token limit; at 1.15, none did in testing, and replies ended on their own at a median of about 400 tokens and at most about 1,100, so a max_tokens of 1200 is enough. The context window is 8192 tokens.

Special Tokens

Token	ID
`<pad>`	0
`<bos>`	1
`<eos>`	2
`<unk>`	3
`<\|im_start\|>`	4
`<\|im_end\|>`	5

Limitations

This is a small research model, built to learn how language models work from the ground up. It is not suitable for production use.

Often wrong. It writes fluent text that is frequently factually incorrect, and it states errors confidently. Do not rely on it for medical, legal, financial or other advice.
Not reliably safe. DPO included safety preference pairs. In red-team tests it declined or redirected 10 of 24 harmful requests, but answered the rest, sometimes after a warning. Pretraining data includes web text and real chatbot conversations, so it can produce offensive, biased or otherwise inappropriate content.
Repetition. Output can loop, especially with greedy decoding; sampling with a temperature helps.
English only.
Limited long-context recall. It was trained on 8192-token sequences, but in passkey-retrieval tests it recalls a specific detail reliably only from about 500 tokens back, about two times in three from 2,000 tokens, and not at all from 4,000 or more.

License

MIT

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Safetensors

Model size

1B params

Tensor type

BF16

redptam
/

tyrian-1b