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

Libraries

How to use AIPixelMedia/astrid with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "AIPixelMedia/astrid")

Transformers

How to use AIPixelMedia/astrid with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIPixelMedia/astrid with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AIPixelMedia/astrid

SGLang

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

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

See axolotl config

axolotl version: 0.13.0.dev0

base_model: meta-llama/Llama-3.1-8B-Instruct

load_in_8bit: false

load_in_4bit: true

strict: false

adapter: lora

datasets:

  - path: AIPixelMedia/astrid-dataset

    data_files: "*formatted.jsonl"

    type: alpaca

dataset_prepared_path: last_run_prepared

val_set_size: 0.1

output_dir: ./outputs/astrid-llama-8b

sequence_len: 2048

sample_packing: true

eval_sample_packing: false

pad_to_sequence_len: true

flash_attention: true

seed: 35

gradient_accumulation_steps: 4

micro_batch_size: 2

num_epochs: 20

optimizer: paged_adamw_32bit

learning_rate: 2e-5

lr_scheduler: cosine

lora_r: 16

lora_alpha: 32

lora_dropout: 0.2

lora_target_modules:

  - q_proj

  - k_proj

  - v_proj

  - o_proj

  - gate_proj

  - up_proj

  - down_proj

lora_modules_to_save:

  - lm_head

merge_lora: false

save_safetensors: true

train_on_inputs: false

group_by_length: true

bf16: auto

tf32: false

gradient_checkpointing: true

gradient_checkpointing_kwargs:

  use_reentrant: false

eval_steps: 5

save_steps: 100

early_stopping_patience: 2

logging_steps: 5

warmup_steps: 10

weight_decay: 0.01

special_tokens:

  pad_token: "<|end_of_text|>"

outputs/astrid-llama-8b

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the AIPixelMedia/astrid-dataset dataset. It achieves the following results on the evaluation set:

Loss: 2.3918
Memory/max Active (gib): 12.13
Memory/max Allocated (gib): 12.13
Memory/device Reserved (gib): 16.52

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 35
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	3.2546	11.95	11.95	12.15
3.1725	1.8889	5	3.2149	12.13	12.13	17.46
3.1171	3.4444	10	2.9429	12.13	12.13	16.52
2.6355	5.0	15	2.6398	12.13	12.13	16.52
2.3752	6.8889	20	2.5206	12.13	12.13	16.52
2.1869	8.4444	25	2.4464	12.13	12.13	16.52
2.0751	10.0	30	2.4187	12.13	12.13	16.52
2.0616	11.8889	35	2.4084	12.13	12.13	16.52
2.0263	13.4444	40	2.3918	12.13	12.13	16.52

Framework versions

PEFT 0.17.1
Transformers 4.57.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

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