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", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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 new

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-bloom-stage2 / README.md

Dev-the-dev91

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	---
	base_model: Qwen/Qwen2.5-7B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	license: apache-2.0
	tags:
	- lora
	- sft
	- transformers
	- trl
	- unsloth
	- bloom-taxonomy
	- sna-learning
	datasets:
	- Dev-the-dev91/sna-regal-training-data
	---

	# 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

	```python
	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)