Instructions to use modrill/montalte_code_think_dataavailable_ls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/montalte_code_think_dataavailable_ls with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/montalte_code_think_dataavailable_ls")
model = AutoModelForCausalLM.from_pretrained("modrill/montalte_code_think_dataavailable_ls")
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

vLLM

How to use modrill/montalte_code_think_dataavailable_ls with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/modrill/montalte_code_think_dataavailable_ls

SGLang

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

Docker Model Runner
How to use modrill/montalte_code_think_dataavailable_ls with Docker Model Runner:
```
docker model run hf.co/modrill/montalte_code_think_dataavailable_ls
```

montalte_code_think_dataavailable_ls

File size: 417 Bytes

d92746e

{
  "base_model": "Qwen/Qwen3-4B-Base",
  "ft_model": "Montalte/code_think",
  "task_name": "coding",
  "sample_size": 64,
  "effective_sample_size": 64,
  "n_epochs": 10,
  "sparsity": 0.1,
  "sigmoid_bias": 3.0,
  "lr": 10000000.0,
  "l1_strength": 1e-05,
  "max_length": 2048,
  "dataless": false,
  "mask_ratio": 0.10025214239450347,
  "save_merged_model": true,
  "algorithm": "repo_exact_localizer_train_mask"
}