GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
RaspizdAI
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
RaspizdAI
Model Tree
unsloth/Qwen2.5-3B-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
tolk-flash is a lightweight Flash version of the tolk-7B language model, based on Qwen 2.5 3B.
The model is designed for fast multi-turn conversational dialogue, persona alignment, and text generation in Russian and English while maintaining a small parameter count for efficient inference.
Model Name: tolk-flash
Parameters: 3B
Base Model: unsloth/Qwen2.5-3B-bnb-4bit
Languages: Russian (ru), English (en)
Primary Task: Conversational Text Generation / Persona-based Dialogue
You can run tolk-flash using standard Hugging Face Transformers:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "RaspizdAI/tolk-flash" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "Привет! Расскажи немного о себе."}] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") outputs = model.generate( input_ids=inputs, max_new_tokens=1024, temperature=0.2, top_p=0.9, do_sample=True) response = tokenizer.decode( outputs[0][inputs.shape[1]:], skip_special_tokens=True) print(response)tolk-flash is intended as a smaller and faster alternative to larger versions of the tolk-7B model.
With 3B parameters, it requires significantly fewer resources and is better suited for:
This model is made available under the MIT License. You are free to use, modify, distribute, and incorporate this model into commercial or non-commercial applications.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "RaspizdAI/tolk-flash" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "Привет! Расскажи немного о себе."}] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") outputs = model.generate( input_ids=inputs, max_new_tokens=1024, temperature=0.2, top_p=0.9, do_sample=True) response = tokenizer.decode( outputs[0][inputs.shape[1]:], skip_special_tokens=True) print(response)