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
```

astrid

File size: 4,250 Bytes

29f1451

---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- axolotl
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- transformers
datasets:
- AIPixelMedia/astrid-dataset
pipeline_tag: text-generation
model-index:
- name: outputs/astrid-llama-8b
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.13.0.dev0`
```yaml
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|>"

```

</details><br>

# outputs/astrid-llama-8b

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/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