Instructions to use ekesel/skillforge-llama-3.2-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ekesel/skillforge-llama-3.2-3b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir skillforge-llama-3.2-3b ekesel/skillforge-llama-3.2-3b
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
SkillForge — LLaMA 3.2 3B Fine-tuned on SkillVault
A domain-specific model trained to generate professional AI agent skill files (SKILL.md) for Claude Code, Cursor, Gemini CLI, and Codex.
What It Does
Given a tool or task description, SkillForge generates a complete, structured SKILL.md file with proper YAML frontmatter, trigger conditions, prerequisites, and practical code examples.
Training
- Base model: meta-llama/Llama-3.2-3B-Instruct
- Method: LoRA fine-tuning via MLX on Apple M4
- Dataset: SkillVault — 1,113 curated SKILL.md files from
trusted repos (Anthropic, LambdaTest, and community collections)
- 10 hand-crafted synthetic examples
- Iterations: 2,000 (1,000 @ 2e-4 LR + 1,000 @ 1e-4 LR)
- Final train loss: 0.656 | Val loss: 1.312
Usage
from transformers import pipeline
pipe = pipeline("text-generation", model="ekaansh/skillforge-llama-3.2-3b")
prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are SkillForge, an expert AI that generates professional agent skill files (SKILL.md).<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Generate a SKILL.md for PostgreSQL database management.
Description: Best practices for queries, schemas, and debugging
Allowed tools: Bash, Read, Write<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>"""
result = pipe(prompt, max_new_tokens=600, temperature=0.7)
print(result[0]['generated_text'])
Dataset
The training dataset (SkillVault) is available at:
ekaansh/skillvault
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Model size
3B params
Tensor type
BF16
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Model tree for ekesel/skillforge-llama-3.2-3b
Base model
meta-llama/Llama-3.2-3B-Instruct