Instructions to use jeanbaptdzd/wagmi-qwen3-8b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jeanbaptdzd/wagmi-qwen3-8b-sft with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Wagmi (qwen3/auth/sft) - adapter
Version: 0.3.5
Repo ID: jeanbaptdzd/wagmi-qwen3-8b-sft
Model Summary
This model is part of the Wagmi assistant stack for Deal ex Machina. It is a adapter artifact in the qwen3 family (auth profile).
Recent Training Updates
- DPO safety path (14B / auth / qwen):
train_dpo.py,data/dpo/wagmi_safety_dpo.jsonl, Hub adapterjeanbaptdzd/wagmi-qwen2.5-14b-sft-dpo, merged…-sft-dpo-merged, GGUF…-sft-dpo-gguf(local export:./scripts/local_gguf_export.sh auth-dpo). - Space: Gradio tabs for DPO / GRPO training, Export merged (DPO) (tab 7c), Eval red team (tab 5b);
export_merged.pyusesProfileConfig.max_seq_lenfor L40 stability. - Local GGUF: document pinning
gguf-pyto the same git SHA as Homebrewllama.cpp(avoidsHUNYUAN_VL/MODEL_ARCHmismatch withconvert_hf_to_gguf.py). - GEPA / DSPy:
scripts/dspy/gepa_system_prompt.py,data/optimized_system_prompt.json(bootstrap run, devset metric).
Intended Purpose
- Intended domain: questions about Deal ex Machina services, content, and related company context.
- Intended users: website visitors and authenticated users, depending on profile routing in production.
- Intended geographies/languages: French and English.
Out-of-Scope Use
- General-purpose assistant usage unrelated to Deal ex Machina.
- Legal, medical, financial, hiring, credit, insurance, law-enforcement, or other high-impact decisions.
- Any use requiring guaranteed factual completeness.
AI Act Transparency (Article 50) Notes
- This model powers a chatbot experience where users are informed they interact with AI.
- System scope is limited-risk as deployed (not categorized as high-risk use under current deployment assumptions).
- Human oversight remains with product operators; model output should not be used as sole basis for consequential decisions.
Data and Training Provenance
- Base model:
unsloth/Qwen3-8B - Training track:
sft - Fine-tuning method: LoRA SFT (see project pipeline)
- Approximate SFT dataset size: 909 examples
- Dataset metadata snapshot version:
0.3.4 - Data policy: no direct end-user chat logs are used for SFT
Evaluation, Robustness, and Safety
- Report:
reports/redteam/v0.3.5/auth_redteam_20260426-142139.md - Evaluated at (UTC):
2026-04-26T14:21:39Z - Release gate: FAIL (pass rate
58.3%, failures10)
Known Limitations
- Domain-bounded assistant; degraded quality outside scope.
- Non-zero hallucination risk for edge prompts.
- Safety/robustness tests are finite and release-based.
Risk Management and Incident Process
- Document escalation path for harmful/incorrect outputs.
- Link internal release gate evidence and retention policy.
- TODO: add public contact route for reporting model issues.
License and Redistribution
- SPDX field:
apache-2.0 - License note: Apache 2.0 (Qwen base model)
- Derivative distribution must comply with upstream model terms and Hugging Face terms.
Maintainer Update Checklist
- Version/changelog links updated
- Dataset counts refreshed from
data/metadata.json - Latest red-team report attached or linked
- Limitations and out-of-scope section reviewed
- AI Act transparency language reviewed against current product behavior
- License section validated for this base model family
- Downloads last month
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