Text Generation
PEFT
Safetensors
English
French
electronics
embedded-systems
platformio
lora
sft
ailiance-tuning
conversational
Instructions to use clemsail/ailiance-platformio-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use clemsail/ailiance-platformio-sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "clemsail/ailiance-platformio-sft") - Notebooks
- Google Colab
- Kaggle
chore: rebrand card to Ailiance
Browse files
README.md
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- platformio
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- lora
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- sft
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-
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language:
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- en
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- fr
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pipeline_tag: text-generation
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---
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#
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Fine-tuned LoRA adapter for **platformio** domain expertise, based on `Qwen/Qwen3-8B`.
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Part of the [
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for the [
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## Training Details
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")
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model = PeftModel.from_pretrained(model, "clemsail/
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
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```
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| Field | Value |
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| Provider |
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| Role under AI Act | GPAI provider for this adapter |
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| Base model | `Qwen/Qwen3-8B` — see upstream provenance |
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| Adapter type | LoRA / PEFT — adapter weights only; base unchanged |
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| Training data origin |
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| License | Apache-2.0 (adapter). Upstream base licence applies separately. |
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| Intended use | PlatformIO build system |
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| Out of scope | Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code |
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| Risk classification | Limited risk — Article 50 transparency obligations apply |
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| Copyright respect | Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data. |
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| Full provenance | https://github.com/
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| Contact | postmaster@saillant.cc — biased output reports, copyright concerns, etc. |
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⚠️ **You are using an AI model.** Outputs may be inaccurate, biased or
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- platformio
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- lora
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- sft
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- ailiance-tuning
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language:
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- en
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- fr
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pipeline_tag: text-generation
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---
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# Ailiance PLATFORMIO SFT — LoRA Adapter
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Fine-tuned LoRA adapter for **platformio** domain expertise, based on `Qwen/Qwen3-8B`.
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Part of the [Ailiance Models Tuning](https://github.com/ailiance/ailiance-models-tuning) pipeline
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for the [Ailiance](https://github.com/ailiance) platform.
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## Training Details
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")
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model = PeftModel.from_pretrained(model, "clemsail/ailiance-platformio-sft")
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
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```
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| Field | Value |
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| Provider | Ailiance (clemsail / electron-rare) |
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| Role under AI Act | GPAI provider for this adapter |
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| Base model | `Qwen/Qwen3-8B` — see upstream provenance |
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| Adapter type | LoRA / PEFT — adapter weights only; base unchanged |
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| Training data origin | Ailiance proprietary technical corpus + curated public docs |
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| License | Apache-2.0 (adapter). Upstream base licence applies separately. |
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| Intended use | PlatformIO build system |
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| Out of scope | Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code |
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| Risk classification | Limited risk — Article 50 transparency obligations apply |
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| Copyright respect | Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data. |
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| Full provenance | https://github.com/ailiance/ailiance/tree/main/docs/provenance |
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| Contact | postmaster@saillant.cc — biased output reports, copyright concerns, etc. |
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⚠️ **You are using an AI model.** Outputs may be inaccurate, biased or
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