How do I fine-tune Llama with Soup CLI?

Install Soup CLI with 'pip install soup-cli', then run 'soup train --base meta-llama/Llama-3.2-1B --task sft --data your_data.json'. Soup supports all Llama variants with optional Unsloth 2-5x speedup.

What is DPO training and how does Soup CLI support it?

DPO (Direct Preference Optimization) aligns LLMs with human preferences without a reward model. Soup CLI supports DPO along with 10 other methods: SFT, GRPO, PPO, KTO, ORPO, SimPO, IPO, Pretrain, Embedding, and Reward Model training.

How do I install Soup CLI?

Install from PyPI: 'pip install soup-cli'. Requires Python 3.9+. Optional extras: 'pip install soup-cli[fast]' for Unsloth speedup, 'pip install soup-cli[serve]' for inference server.

What models does Soup CLI support?

Soup CLI supports Llama 3.2, Qwen 3, DeepSeek V3/R1, Mistral, Phi-4, Gemma 3, and any Hugging Face transformers-compatible model. 30 ready-made recipes included. It works with both full fine-tuning and QLoRA/LoRA methods.

Can I migrate from LLaMA-Factory or Axolotl to Soup CLI?

Yes. Run 'soup migrate --from llamafactory config.yaml' or 'soup migrate --from axolotl config.yml' to automatically convert your existing training config to Soup format. Also supports Unsloth notebook migration.

Data Tools

Soup includes powerful CLI tools for preparing training datasets.

Inspect

bash

soup data inspect ./data/train.jsonl

Shows dataset statistics: sample count, token distribution, field analysis. For vision datasets, automatically shows image statistics (count, formats, missing files).

Validate

bash

soup data validate ./data/train.jsonl
soup data validate ./data/train.jsonl --format alpaca

Checks for missing fields, encoding issues, and format compliance. Auto-detects format when --format is not specified.

Convert

bash

soup data convert ./data/train.jsonl --to sharegpt --output converted.jsonl

Transform between alpaca, sharegpt, and chatml formats.

Merge

bash

soup data merge data1.jsonl data2.jsonl --output merged.jsonl --shuffle

Combine multiple datasets with optional shuffling.

Deduplicate

bash

# Requires: pip install 'soup-cli[data]'
soup data dedup ./data/train.jsonl --threshold 0.8

Remove near-duplicate samples using MinHash.

Extended Statistics

bash

soup data stats ./data/train.jsonl

Length distribution with histograms, token counts, and language detection.

Synthetic Data Generation

bash

# Generate using OpenAI API
soup data generate --prompt "Create math word problems" --count 100 --format alpaca

# Use a different model
soup data generate --prompt "Medical Q&A pairs" --model gpt-4o --count 500

# Deduplicate against existing data
soup data generate --prompt "..." --count 200 --dedup-with existing.jsonl

# Use seed examples to guide style
soup data generate --prompt "..." --seed examples.jsonl --count 100

# Use a local server (soup serve, Ollama, etc.)
soup data generate --prompt "..." --provider server --api-base http://localhost:11434/v1

Multi-Provider Support (v0.20.0+)

bash

# Generate via local Ollama instance
soup data generate --prompt "..." --provider ollama --model llama3.1
soup data generate --prompt "..." --ollama-model llama3.1  # shorthand

# Generate via Anthropic Claude API (set ANTHROPIC_API_KEY env var)
soup data generate --prompt "..." --provider anthropic --model claude-3-haiku-20240307

# Generate via local vLLM server
soup data generate --prompt "..." --provider vllm --model meta-llama/Llama-3.1-8B-Instruct

Domain Templates (v0.20.0+)

bash

# Code instruction pairs (Python, JS, Go, Rust, Java)
soup data generate --prompt "..." --template code --language Python --task-type function

# Multi-turn conversations
soup data generate --prompt "..." --template conversation --turns 6 --topic "science"

# QA from context document
soup data generate --prompt "..." --template qa --context document.txt

# Preference data (DPO/KTO/ORPO)
soup data generate --prompt "..." --template preference --pref-task dpo

# Chain-of-thought reasoning (GRPO)
soup data generate --prompt "..." --template reasoning --domain math

Quality Pipeline (v0.20.0+)

bash

# Auto-validate after generation (remove malformed entries)
soup data generate --prompt "..." --validate

# Auto-filter by quality (coherence scoring)
soup data generate --prompt "..." --filter

# Auto-dedup (MinHash, requires: pip install 'soup-cli[data]')
soup data generate --prompt "..." --dedup

# Full quality pipeline: validate + filter + dedup
soup data generate --prompt "..." --quality-pipeline

Quality Filter

bash

# Filter by coherence score
soup data filter ./data/train.jsonl --coherence 0.3

# Filter by perplexity + coherence
soup data filter ./data/train.jsonl --perplexity 500 --coherence 0.3

# Add scores without removing samples
soup data filter ./data/train.jsonl --score-only

Uses perplexity + coherence scoring to identify low-quality samples.

Data Sampling (v0.23.0+)

bash

# Random sample
soup data sample ./data/train.jsonl --strategy random --count 1000

# Diverse sample (TF-IDF clustering)
soup data sample ./data/train.jsonl --strategy diverse --count 500

# Hard examples (by length)
soup data sample ./data/train.jsonl --strategy hard --count 500

Data Splitting (v0.23.0+)

bash

# Split into train/val/test
soup data split ./data/train.jsonl --ratio 0.8,0.1,0.1

# Stratified split
soup data split ./data/train.jsonl --ratio 0.9,0.1 --stratify

HuggingFace Dataset Hub (v0.24.0+)

bash

# Search for datasets
soup data search "math reasoning"

# Preview remote dataset metadata
soup data preview tatsu-lab/alpaca

# Download to local JSONL
soup data download tatsu-lab/alpaca --output ./data/alpaca.jsonl --samples 1000

Dataset Registry (v0.24.0+)

bash

# Register a dataset
soup data register my-chat-data --path ./data/chat.jsonl --format chatml

# List registered datasets
soup data registry

# Use in config: data.train: registry:my-chat-data
soup data unregister my-chat-data