Instructions to use auryn-macmillan/boostedv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use auryn-macmillan/boostedv1 with Transformers:

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

pipe = pipeline("text-generation", model="auryn-macmillan/boostedv1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("auryn-macmillan/boostedv1")
model = AutoModelForCausalLM.from_pretrained("auryn-macmillan/boostedv1", 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 auryn-macmillan/boostedv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "auryn-macmillan/boostedv1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "auryn-macmillan/boostedv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/auryn-macmillan/boostedv1

SGLang

How to use auryn-macmillan/boostedv1 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 "auryn-macmillan/boostedv1" \
    --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": "auryn-macmillan/boostedv1",
		"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 "auryn-macmillan/boostedv1" \
        --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": "auryn-macmillan/boostedv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use auryn-macmillan/boostedv1 with Docker Model Runner:
```
docker model run hf.co/auryn-macmillan/boostedv1
```

BoostedV1

Continued LoRA training of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B for improved reasoning and code generation. This model is the merged output of the boostedv1train pipeline: 400 MLX LoRA steps (Apple Silicon) + 550 Phase-1 continuation steps (dual RTX 3090, bf16).

Architecture

Base: DeepSeek-R1-Distill-Qwen-1.5B (Qwen2ForCausalLM, 1.5B params)
LoRA rank 8, alpha 160, on layers 20-27 (q/k/v/o + gate/up/down)
Merged into a standalone model (no LoRA needed at inference)

Training

Stage	Platform	Steps	Batch	Seq len	Data
MLX run 1	Apple Silicon	150	4	512	OpenCodeInstruct
MLX run 2	Apple Silicon	250	2	1024	OpenCodeInstruct
Phase 1	2x RTX 3090	550	16 (eff)	2048	OpenThoughts + OpenR1-Math + OpenCodeInstruct

Evaluation

Benchmark	Score
GSM8K	46.0%
HumanEval (pass@1)	7.3%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('auryn-macmillan/boostedv1') tok = AutoTokenizer.from_pretrained('auryn-macmillan/boostedv1') inputs = tok('What is 2+2?', return_tensors='pt') out = model.generate(**inputs, max_new_tokens=128) print(tok.decode(out[0]))

Notes

Standard Qwen2 architecture, no custom code, no trust_remote_code needed.
Trained in an isolated container; repo contains no training code or data.

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Safetensors

Model size

2B params

Tensor type

BF16