Instructions to use Dev-the-dev91/sna-bloom-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dev-the-dev91/sna-bloom-stage2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "Dev-the-dev91/sna-bloom-stage2")

Transformers

How to use Dev-the-dev91/sna-bloom-stage2 with Transformers:

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

pipe = pipeline("text-generation", model="Dev-the-dev91/sna-bloom-stage2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dev-the-dev91/sna-bloom-stage2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dev-the-dev91/sna-bloom-stage2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dev-the-dev91/sna-bloom-stage2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dev-the-dev91/sna-bloom-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dev-the-dev91/sna-bloom-stage2

SGLang

How to use Dev-the-dev91/sna-bloom-stage2 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 "Dev-the-dev91/sna-bloom-stage2" \
    --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": "Dev-the-dev91/sna-bloom-stage2",
		"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 "Dev-the-dev91/sna-bloom-stage2" \
        --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": "Dev-the-dev91/sna-bloom-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dev-the-dev91/sna-bloom-stage2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Dev-the-dev91/sna-bloom-stage2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Dev-the-dev91/sna-bloom-stage2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dev-the-dev91/sna-bloom-stage2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dev-the-dev91/sna-bloom-stage2",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dev-the-dev91/sna-bloom-stage2 with Docker Model Runner:
```
docker model run hf.co/Dev-the-dev91/sna-bloom-stage2
```

SNA Learning — Bloom's Taxonomy Stage 2 (Apply + Analyze)

LoRA adapter for personalized CS/ML concept teaching using Bloom's Taxonomy scaffolding and Netflix-anchored memory palaces.

Training Details

Base model: Qwen/Qwen2.5-7B-Instruct (4-bit via Unsloth)
Stage: 2 of 3 (Apply + Analyze levels), continuing from Stage 1 (Remember + Understand)
Method: SFT with LoRA (rank 32, alpha 32, dropout 0.05)
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs: 3
Learning rate: 1e-4 (cosine schedule, 4 warmup steps)
Batch size: 1 × 8 gradient accumulation
Max sequence length: 1024
Precision: bf16
Hardware: Modal (GPU)
Training time: ~~789s (~~13 min)
Framework: TRL + PEFT + Unsloth

Metrics

Metric	Value
Train loss (avg)	0.5397
Train loss (final step)	0.4071
Eval loss	0.7310
Grad norm (final)	0.336
Total steps	165

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "Dev-the-dev91/sna-bloom-stage2")
tokenizer = AutoTokenizer.from_pretrained("Dev-the-dev91/sna-bloom-stage2")

Bloom's Levels Covered

Stage 1: Remember + Understand (recall, explain, mnemonic, song)
Stage 2 (this): Apply + Analyze (scenario walkthrough, component decomposition)
Stage 3: Evaluate + Create (planned)

Downloads last month: 5

Model tree for Dev-the-dev91/sna-bloom-stage2

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2423)

this model