Instructions to use podstack/Jedi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use podstack/Jedi with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="podstack/Jedi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("podstack/Jedi")
model = AutoModelForCausalLM.from_pretrained("podstack/Jedi", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use podstack/Jedi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "podstack/Jedi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "podstack/Jedi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/podstack/Jedi

SGLang

How to use podstack/Jedi with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "podstack/Jedi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "podstack/Jedi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "podstack/Jedi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "podstack/Jedi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use podstack/Jedi with Docker Model Runner:
```
docker model run hf.co/podstack/Jedi
```

Jedi / train.py

podstack

Jedi v1.0

7a74082 verified 1 day ago

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951 Bytes

	"""Stage 3: full fine-tune of Qwen2.5-0.5B-Instruct into Jedi (loss on the one-word verdict only)."""
	import os
	from datasets import load_from_disk
	from trl import SFTConfig, SFTTrainer

	BASE = os.environ.get("BASE", "Qwen/Qwen2.5-0.5B-Instruct")
	ds = load_from_disk("data/jedi_judge").select_columns(["prompt", "completion"])

	cfg = SFTConfig(
	output_dir="ckpt", num_train_epochs=2, per_device_train_batch_size=8, gradient_accumulation_steps=4,
	learning_rate=2e-5, lr_scheduler_type="cosine", warmup_steps=30, weight_decay=0.0, bf16=True,
	max_length=2048, completion_only_loss=True, logging_steps=20, eval_strategy="epoch", save_strategy="epoch",
	save_total_limit=1, report_to=[], gradient_checkpointing=True, dataloader_num_workers=4,
	)
	trainer = SFTTrainer(model=BASE, args=cfg, train_dataset=ds["train"], eval_dataset=ds["val"])
	trainer.train()
	trainer.save_model("jedi-0.5b")
	trainer.processing_class.save_pretrained("jedi-0.5b")