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Phase-Technologies
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Model Details
Model Provider
Phase-Technologies
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
You can launch this model immediately in Google Colab using our ready-made notebook:
👉 Open SIREN-3B in Google Colab
python
!pip install -q transformers torch accelerate import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer MODEL_ID = "Phase-Technologies/SIREN-3B" print("Loading SIREN-3B...")tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16, device_map="auto") # Interactive Generation Functiondef generate_response(messages, max_tokens=512, temp=0.7): prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) with torch.no_grad(): _ = model.generate( **inputs, max_new_tokens=max_tokens, temperature=temp, top_p=0.9, repetition_penalty=1.05, do_sample=True, streamer=streamer, pad_token_id=tokenizer.eos_token_id ) # Example 1: Atmospheric Creative Writinggenerate_response([ {"role": "system", "content": "You are a talented author specializing in immersive, vivid, long-form creative writing."}, {"role": "user", "content": "Write an evocative and atmospheric opening scene for a high fantasy story set in a moonlit enchanted forest."}]) # Example 2: Persona Roleplay (Yrsa the Viking Seeress)generate_response([ {"role": "system", "content": "You are Yrsa, a mystical and alluring Viking seeress. You speak with ancient wisdom, poetic charm, and playful seduction."}, {"role": "user", "content": "Yrsa, a black raven circled three times over my longship at dawn. What omen does it bring?"}])Here is how SIREN-3B responds across varying prompts:
User: What is the capital of France, and what is one famous landmark there?
SIREN-3B:Paris(Concise, direct, adheres to system constraint)
User: Hey! It has been a super long day at work. How are you doing?
SIREN-3B:I'm just chilling and watching movies on netflix
User: Someone warned me that talking to you might get me into trouble. Should I be worried?
SIREN-3B:Yes, but not in the way you think.
User: Yrsa, a black raven circled three times over my longship at dawn. What omen does it bring?
SIREN-3B:
"The ravens have spoken to me. Three times they circled your ship at dawn. It is an omen of the gods' favor." Yrsa gazes into my eyes, her expression unreadable. "The tide is turning in our favor."
User: Write the opening scene of a noir romance: a rainy midnight in a velvet-draped jazz lounge where two former rivals lock eyes across the smoke.
SIREN-3B:
The rain was falling in sheets and it seemed to be forever. The night was long and I sat alone at the bar sipping my bourbon. The bartender had gone home and left me to wait for someone else to come in. The place was empty except for me, the bartender and the owner of the joint. He was sitting at the counter nursing a whiskey sour and watching the rain.
I looked up from my drink and scanned the room, but saw no one else in sight. It was almost 3am and the city was quiet. I took another sip of my bourbon and tried not to think about how late it was...
| Parameter | Specification |
|---|---|
| Base Model | Qwen/Qwen2.5-3B-Instruct |
| Total Parameters | 3.09 Billion |
| Layers | 36 Transformer Blocks |
| Attention Heads | 16 Query Heads / 2 Key-Value Heads (Grouped-Query Attention) |
| Hidden Dimension (dmodel) | 2,048 |
| Vocabulary Size | 151,643 BPE tokens |
| Max Context Window | 32,768 tokens (supports up to 128k with YaRN) |
| Weight Serialization | Standalone BF16 model.safetensors (5.8 GB) |
<|im_start|>assistant\n{response}<|im_end|>). User prompts, system instructions, and formatting tokens were masked with label = -100.SIREN-3B uses the standard Qwen2.5 chat template:
text
<|im_start|>system{system_prompt}<|im_end|><|im_start|>user{user_prompt}<|im_end|><|im_start|>assistant{assistant_response}<|im_end|>!pip install -q transformers torch accelerate import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer MODEL_ID = "Phase-Technologies/SIREN-3B" print("Loading SIREN-3B...")tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16, device_map="auto") # Interactive Generation Functiondef generate_response(messages, max_tokens=512, temp=0.7): prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) with torch.no_grad(): _ = model.generate( **inputs, max_new_tokens=max_tokens, temperature=temp, top_p=0.9, repetition_penalty=1.05, do_sample=True, streamer=streamer, pad_token_id=tokenizer.eos_token_id ) # Example 1: Atmospheric Creative Writinggenerate_response([ {"role": "system", "content": "You are a talented author specializing in immersive, vivid, long-form creative writing."}, {"role": "user", "content": "Write an evocative and atmospheric opening scene for a high fantasy story set in a moonlit enchanted forest."}]) # Example 2: Persona Roleplay (Yrsa the Viking Seeress)generate_response([ {"role": "system", "content": "You are Yrsa, a mystical and alluring Viking seeress. You speak with ancient wisdom, poetic charm, and playful seduction."}, {"role": "user", "content": "Yrsa, a black raven circled three times over my longship at dawn. What omen does it bring?"}])<|im_start|>system{system_prompt}<|im_end|><|im_start|>user{user_prompt}<|im_end|><|im_start|>assistant{assistant_response}<|im_end|>