ソース情報
- リポジトリ
- ffsshhttiikk/opencode-agents-skills
- ソースの最終更新活動
- 2026年2月28日 22:52
- 検出された SKILL.md の言語
- 英語
- スター
- 3
- フォーク
- 2
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- large-language-models
- description
- Large language models
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"machine-learning-engineers","category":"artificial-intelligence"}
## What I do
- Work with large language models
- Implement prompt engineering
- Fine-tune LLM models
- Build RAG systems
- Optimize LLM inference
- Handle LLM hallucination
## When to use me
Use me when:
- Building LLM-powered applications
- Implementing chatbots
- Creating text generation systems
- Knowledge-intensive question answering
- Text classification with LLMs
## Key Concepts
### LLM Architecture
- **Transformer**: Self-attention based
- **Encoder-only**: BERT, RoBERTa
- **Decoder-only**: GPT, LLaMA, Mistral
- **Encoder-decoder**: T5, BART
### Prompt Engineering
```python
# Few-shot prompting
prompt = """Classify the sentiment as POSITIVE, NEGATIVE, or NEUTRAL.
Text: This product is amazing!
Sentiment: POSITIVE
Text: Terrible experience, would not recommend.
Sentiment: NEGATIVE
Text: It was okay, nothing special.
Sentiment: NEUTRAL
Text: I absolutely love this!
Sentiment:"""
# Chain-of-thought
cot_prompt = """Solve the problem step by step.
Problem: If there are 5 birds and you shoot 2, how many remain?
Solution: First, 5 - 2 = 3. Then, the remaining birds fly away because of the noise, so 0 remain. Answer: 0
Problem: John has 3 apples. He buys 5 more. Then he eats 2. How many?
Solution:"""
```
### Fine-tuning
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
# LoRA fine-tuning
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b")
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type=TaskType.CAUSAL_LM
)
model = get_peft_model(model, lora_config)
# Fine-tune on custom dataset
```
### RAG (Retrieval-Augmented Generation)
```python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Build retriever
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever()
# Create QA chain
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=retriever
)
answer = qa.run(query)
```
### Challenges
- Hallucination
- Context length limits
- Token costs
- Latency
- Bias and safety
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