Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add liortesta/ClawdAgent --skill model-merginggit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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/liortesta/clawdagent/model-merging)<a href="https://agentmods.dev/skills/liortesta/clawdagent/model-merging"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/model-merging.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.03685 |
| Opus 5 | $0.00036 | $0.01843 |
| Sonnet 5 | $0.00015 | $0.00737 |
| Haiku 4.5 | $0.00007 | $0.00368 |
Grade A, and why
model-merging 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 3d 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.
This is a copy
100% identical to model-merging — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 540 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Merging: Combining Pre-trained Models
When to Use This Skill
Use Model Merging when you need to:
- Combine capabilities from multiple fine-tuned models without retraining
- Create specialized models by blending domain-specific expertise (math + coding + chat)
- Improve performance beyond single models (often +5-10% on benchmarks)
- Reduce training costs - no GPUs needed, merges run on CPU
- Experiment rapidly - create new model variants in minutes, not days
- Preserve multiple skills - merge without catastrophic forgetting
Success Stories: Marcoro14-7B-slerp (best on Open LLM Leaderboard 02/2024), many top HuggingFace models use merging
Tools: mergekit (Arcee AI), LazyMergekit, Model Soup
Installation
# Install mergekit
git clone https://github.com/arcee-ai/mergekit.git
cd mergekit
pip install -e .
# Or via pip
pip install mergekit
# Optional: Transformer library
pip install transformers torch
Quick Start
Simple Linear Merge
# config.yml - Merge two models with equal weights
merge_method: linear
models:
- model: mistralai/Mistral-7B-v0.1
parameters:
weight: 0.5
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
weight: 0.5
dtype: bfloat16
# Run merge
mergekit-yaml config.yml ./merged-model --cuda
# Use merged model
python -m transformers.models.auto --model_name_or_path ./merged-model
SLERP Merge (Best for 2 Models)
# config.yml - Spherical interpolation
merge_method: slerp
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 32]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
parameters:
t: 0.5 # Interpolation factor (0=model1, 1=model2)
dtype: bfloat16
Core Concepts
1. Merge Methods
Linear (Model Soup)
- Simple weighted average of parameters
- Fast, works well for similar models
- Can merge 2+ models
merged_weights = w1 * model1_weights + w2 * model2_weights + w3 * model3_weights
# where w1 + w2 + w3 = 1
What ships with it
3 files 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.
- 3d ago First seen · 540 lines · 73 tokens per session scan A 39bcead42b69
model-merging is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 10d ago), licensed Apache-2.0. It adds 73 tokens to every session and 3,685 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to model-merging, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task…
knowledge-distillation
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and…