model-fine-tuning skillA
model-fine-tuning is agent-read markdown (skill) from jnpiyush/agentx: Fine-tune foundation models for domain-specific tasks. Use when adapting LLMs with LoRA/QLoRA/PEFT, full fine-tuning, knowledge distillation, or designing training data pipelines for model customization..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Model Fine-Tuning > **Purpose**: Adapt pre-trained foundation models to domain-specific tasks through parameter-efficient and full fine-tuning techniques. --- ## When to Use This Skill - Adapting an LLM to follow domain-specific instructions or style - Improving model accuracy on specialized tasks (classification, extraction, generation) - Reducing inference cost by distilling a large model into a smaller one - Building training data pipelines for fine-tuning datasets - Choosing between fine-tuning approaches (LoRA, QLoRA, full, distillation) ## Prerequisites - Base model selection (open-source or API-based) - Training dataset (instruction pairs, labeled examples, or preference data) - GPU compute (local or cloud) ## Decision Tree ``` Need model customization? +- Task-specific behavior (format, style, domain)? | +- Small dataset (<1K examples)? | +- Use few-shot prompting first -> Good enough? -> Done | +- Not enough? -> LoRA fine-tuning (efficient, low data) | +- Medium dataset (1K-50K examples)? | +- Use LoRA or QLoRA -> Most cost-effective | +- Large dataset (50K+ examples)? | +- Full fine-tuning (if budget allows) …
Read the whole file at its exact version.
How to install
mdr add jnpiyush/agentx/model-fine-tuning@v1.0.0mdr add jnpiyush/agentx/model-fine-tuning@sha256:da24ac69e0849b28Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_anz65eradifimbjp)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-09-15 | f17c505 | 10,326 B | A | view · diff |
| v1.0.0 | 2026-03-01 | 969f335 | 10,324 B | A | view · diff |
| v1.0.0 | 2026-03-01 | ac76f34 | 9,307 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (10326 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
jnpiyush/agentx · 16 stars · license Apache-2.0 · pushed 2026-09-21 · branch master
API
GET https://markdownregistry.com/api/v1/artifacts/art_anz65eradifimbjp GET https://markdownregistry.com/api/v1/resolve?ref=jnpiyush/agentx/model-fine-tuning GET https://markdownregistry.com/api/v1/blob/da24ac69e0849b280b0f7838f82c7d54d6e6fa86a544105e2fbf018c824f49e1
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