Instructions to use schneewolflabs/A1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/A1.1 with Transformers:

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

pipe = pipeline("text-generation", model="schneewolflabs/A1.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("schneewolflabs/A1.1")
model = AutoModelForCausalLM.from_pretrained("schneewolflabs/A1.1")
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 schneewolflabs/A1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/A1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schneewolflabs/A1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/schneewolflabs/A1.1

SGLang

How to use schneewolflabs/A1.1 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 "schneewolflabs/A1.1" \
    --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": "schneewolflabs/A1.1",
		"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 "schneewolflabs/A1.1" \
        --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": "schneewolflabs/A1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use schneewolflabs/A1.1 with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/A1.1
```

nbeerbower commited on 21 days ago

Commit

598cb28

verified ·

1 Parent(s): 4661452

Add model card with training configuration

Browse files

Files changed (1) hide show

README.md +56 -0

README.md ADDED Viewed

	@@ -0,0 +1,56 @@

+---
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- merlina
+- grimoire
+- text-generation
+- sft
+datasets:
+- Crownelius/Opus-4.6-Reasoning-3300x
+- schneewolflabs/Luna-DPO
+- schneewolflabs/i-SFT
+base_model:
+- schneewolflabs/A1
+---
+# A1.1
+## Training Configuration
+| Parameter | Value |
+|-----------|-------|
+| Training Mode | SFT |
+| Base Model | `schneewolflabs/A1` |
+| Learning Rate | 2e-05 |
+| Epochs | 1 |
+| Batch Size | 2 |
+| Gradient Accumulation | 16 |
+| Effective Batch Size | 32 |
+| Max Sequence Length | 8192 |
+| Optimizer | paged_adamw_8bit |
+| LR Scheduler | cosine |
+| Warmup Ratio | 0.05 |
+| Weight Decay | 0.01 |
+| Max Grad Norm | 0.5 |
+| Seed | 42 |
+| LoRA Rank (r) | 128 |
+| LoRA Alpha | 256 |
+| LoRA Dropout | 0.05 |
+| Target Modules | up_proj, down_proj, gate_proj, k_proj, q_proj, v_proj, o_proj |
+| Quantization | 4-bit (NF4) |
+| GPU | NVIDIA GB10 |
+## Datasets
+Trained on 3 concatenated datasets:
+1. [`Crownelius/Opus-4.6-Reasoning-3300x`](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-3300x) (split: `train`)
+2. [`schneewolflabs/Luna-DPO`](https://huggingface.co/datasets/schneewolflabs/Luna-DPO) (split: `train`)
+3. [`schneewolflabs/i-SFT`](https://huggingface.co/datasets/schneewolflabs/i-SFT) (split: `train`)
+---
+![Trained with Merlina](https://raw.githubusercontent.com/Schneewolf-Labs/Merlina/refs/heads/main/frontend/madewithmerlina_smol.png)
+[Merlina on GitHub](https://github.com/Schneewolf-Labs/Merlina)