Instructions to use DCAgent/a1-stack_jest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DCAgent/a1-stack_jest with Transformers:

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

pipe = pipeline("text-generation", model="DCAgent/a1-stack_jest")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DCAgent/a1-stack_jest")
model = AutoModelForCausalLM.from_pretrained("DCAgent/a1-stack_jest")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DCAgent/a1-stack_jest with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DCAgent/a1-stack_jest"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DCAgent/a1-stack_jest",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DCAgent/a1-stack_jest

SGLang

How to use DCAgent/a1-stack_jest 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 "DCAgent/a1-stack_jest" \
    --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": "DCAgent/a1-stack_jest",
		"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 "DCAgent/a1-stack_jest" \
        --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": "DCAgent/a1-stack_jest",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DCAgent/a1-stack_jest with Docker Model Runner:
```
docker model run hf.co/DCAgent/a1-stack_jest
```

a1-stack_jest

File size: 545 Bytes

e5c3150

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    "achieved_tflops_per_gpu": 0.003747713620293238,
    "achieved_tflops_per_gpu_theoretical": 603.0470121825239,
    "epoch": 7.0,
    "loss_nan_ranks": 0,
    "loss_rank_avg": 0.1903749257326126,
    "mfu_percent": 0.0002648560862398048,
    "mfu_percent_theoretical": 42.618163405125365,
    "total_flos": 1474549610774528.0,
    "train_loss": 0.20471333478722384,
    "train_runtime": 24590.8199,
    "train_samples_per_second": 2.851,
    "train_steps_per_second": 0.178,
    "valid_targets_mean": 5778.1,
    "valid_targets_min": 3072
}