78 lines
1.9 KiB
Markdown
78 lines
1.9 KiB
Markdown
---
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license: other
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license_name: "inherits-base-model-and-dataset-terms"
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base_model: "Qwen/Qwen2.5-0.5B-Instruct"
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library_name: transformers
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pipeline_tag: "text-generation"
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tags:
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- "ai-model-builder"
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- "fine-tuned"
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- reallexi
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- slm
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- "text-generation"
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---
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# reallexi/lexi-rm-agent
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A standalone model of 495M parameters, derived from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
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## Size and requirements
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| Parameters | 495,114,112 (495M) |
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| Weights on disk | 953 MB |
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| Trained context length | 512 tokens |
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| Base model | `Qwen/Qwen2.5-0.5B-Instruct` |
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Approximate memory to hold the weights. Add context and runtime overhead on top.
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| Precision | Weights |
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|---|---|
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| FP16 / BF16 | 944 MB |
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| 8-bit (Q8_0) | 472 MB |
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| 4-bit (Q4_K_M) | 260 MB |
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## Training
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| Strategy | slm |
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| Adapter | Auto LoRA |
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| LoRA rank / alpha | 8 / 16 |
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| Dataset | `bitext/Bitext-customer-support-llm-chatbot-training-dataset` |
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| Samples learned | 100,000 (through phase 382 of 382) |
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| Training steps | 1,250 |
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| Epochs | 5 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-rm-agent")
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tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-rm-agent")
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```
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## License and attribution
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The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
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- Base model: [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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- Training data: `bitext/Bitext-customer-support-llm-chatbot-training-dataset`
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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Produced by Reallexi LLC AI Model Builder from training job #1272.
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Core: https://llm.reallexi.io
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