63 lines
1.3 KiB
Markdown
63 lines
1.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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- ru
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base_model:
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- Qwen/Qwen2.5-0.5B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- Text
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- Smart
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- Slt
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- Small
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- AI
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- Russian
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- English
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- Mini
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---
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# SLT-0.5b-GoToSpeak
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A small 0.5B parameter conversational model based on Qwen2.5-0.5B.
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## Training Dataset
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The model was fine-tuned on 7500 examples.
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The dataset includes:
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- Conversational dialogues in Russian and English
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- Up-to-date general knowledge (as of 2025-2026)
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- Simple Python coding tasks
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- Basic mathematics with step-by-step explanations
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Training method: Supervised Fine-Tuning (SFT).
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "SLT-AI/SLT-0.5b-GoToSpeak"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [{"role": "user", "content": "Hello!"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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