FastVideo is a framework for training and running accelerated video-generation models, including real-time inference and post-training workflows. It is for researchers and developers building or deploying diffusion-based systems that generate video.
Borrowing it
Nothing to install: this file belongs to hao-ai-lab/FastVideo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/.agents/skills/add-model-07-conversion/SKILL.mdgit clone --depth 1 https://github.com/hao-ai-lab/FastVideoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-07-conversion)<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-07-conversion"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-07-conversion/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hao-ai-lab/fastvideo/add-model-07-conversion"><img src="https://agentmods.dev/badge/skills/hao-ai-lab/fastvideo/add-model-07-conversion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01884 |
| Opus 5 | $0.00019 | $0.00942 |
| Sonnet 5 | $0.00008 | $0.00377 |
| Haiku 4.5 | $0.00004 | $0.00188 |
Grade A, and why
add-model-07-conversion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Model Conversion
Goal
Convert official weights into a FastVideo-loadable component layout after Phase 4 native prototypes exist. The conversion script owns parameter mapping, component splitting, passthrough assets, config emission, and strict-load verification.
Inputs
Follow ../add-model/shared/common_rules.md for token/auth safety, state files,
escape hatches, production boundaries, and skip/pass semantics.
Require the initial request from
../add-model/contracts/conversion_request.md.
If the FastVideo key/shape dump is missing, return to /add-model Phase 4. Do
not write a final mapping against an unimplemented component.
For Phase 6 retry requests from component skills, also require the retry shape
from ../add-model/contracts/conversion_request.md.
Output
scripts/checkpoint_conversion/<family>_to_diffusers.py.converted_weights/<family>/withmodel_index.jsonand per-component subfolders.- Updated
tests/local_tests/<model_family>/README.mdwith conversion command, source layout, output path, and strict-load status. - Updated
tests/local_tests/<model_family>/PORT_STATUS.mdwith conversion state, retry history, open questions, and issues/blockers.
Reference Scripts
scripts/checkpoint_conversion/convert_ltx2_weights.py: component prefix splitting, metadata config extraction, passthrough Gemma/tokenizer assets, and optional component-only output.scripts/checkpoint_conversion/stable_audio_to_diffusers.py: monolithicmodel.safetensorssplit into transformer/VAE/conditioner, plus copied passthrough subfolders. Use this shape for single-checkpoint official repos.scripts/checkpoint_conversion/convert_gamecraft_full.py: separate official sources for transformer, VAE, text encoders, tokenizers, scheduler, and rootmodel_index.json.scripts/checkpoint_conversion/longcat_to_fastvideo.py: fused QKV/KV split, renamed native transformer weights, and copied existing Diffusers components.scripts/checkpoint_conversion/pt_to_safetensors.py: simple.ptextraction helper for nested checkpoint dictionaries.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 187 lines · 39 tokens per session scan A e5f0c5e600dc
add-model-07-conversion is a skill published in the GitHub repository hao-ai-lab/FastVideo (4,363 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 1,884 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training.
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Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.