Instructions to use UsernameJustAnother/Nemo-12B-Marlin-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UsernameJustAnother/Nemo-12B-Marlin-v2 with Transformers:

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

pipe = pipeline("text-generation", model="UsernameJustAnother/Nemo-12B-Marlin-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UsernameJustAnother/Nemo-12B-Marlin-v2")
model = AutoModelForCausalLM.from_pretrained("UsernameJustAnother/Nemo-12B-Marlin-v2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UsernameJustAnother/Nemo-12B-Marlin-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UsernameJustAnother/Nemo-12B-Marlin-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UsernameJustAnother/Nemo-12B-Marlin-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UsernameJustAnother/Nemo-12B-Marlin-v2

SGLang

How to use UsernameJustAnother/Nemo-12B-Marlin-v2 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 "UsernameJustAnother/Nemo-12B-Marlin-v2" \
    --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": "UsernameJustAnother/Nemo-12B-Marlin-v2",
		"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 "UsernameJustAnother/Nemo-12B-Marlin-v2" \
        --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": "UsernameJustAnother/Nemo-12B-Marlin-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use UsernameJustAnother/Nemo-12B-Marlin-v2 with Docker Model Runner:
```
docker model run hf.co/UsernameJustAnother/Nemo-12B-Marlin-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: UsernameJustAnother
License: apache-2.0
Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407

Experimental RP Finetune with secret sauce dataset, rsLoRA, r = 256, on an Colab A100 instance. 36GB vRAM used, 2 epochs ~ 3.5hrs of training.

This is for A/B testing vs Marlin v1, to see what difference rank 256 (v2) has compared to rank 64 (v1).

==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
   \\   /|    Num examples = 8,160 | Num Epochs = 2
O^O/ \_/ \    Batch size per device = 2 | Gradient Accumulation steps = 4
\        /    Total batch size = 8 | Total steps = 2,040
 "-____-"     Number of trainable parameters = 912,261,120

        r = 256, 
        target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                          "gate_proj", "up_proj", "down_proj",],
        lora_alpha = 16, 
        lora_dropout = 0, # Supports any, but = 0 is optimized
        bias = "none",    # Supports any, but = "none" is optimized
        use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
        random_state = 3407,
        use_rslora = True,  # lora_alpha --> 16
        loftq_config = None,

        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 5,
        num_train_epochs = 2, 
        learning_rate = 2e-5, # down from 2e-4, could go down to (5e-5 then 1e-5)
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit", 
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 18

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for UsernameJustAnother/Nemo-12B-Marlin-v2

Base model

unsloth/Mistral-Nemo-Instruct-2407

Finetuned

(39)

this model

Quantizations

3 models