Instructions to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 with Transformers:

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

pipe = pipeline("text-generation", model="CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1")

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

tokenizer = AutoTokenizer.from_pretrained("CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1")
model = AutoModelForCausalLM.from_pretrained("CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1", device_map="auto")

PEFT
How to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1

SGLang

How to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 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 "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1 with Docker Model Runner:
```
docker model run hf.co/CycleCoreTechnologies/Maaza-MLM-135M-JSON-v1
```

CycleCore-Technologies commited on Nov 23, 2025

Commit

561fc52

verified ·

1 Parent(s): 68720cb

Upload adapter_config.json with huggingface_hub

Browse files

Files changed (1) hide show

adapter_config.json +30 -45

adapter_config.json CHANGED Viewed

@@ -1,46 +1,31 @@
 {
-  "alora_invocation_tokens": null,
-  "alpha_pattern": {},
-  "arrow_config": null,
-  "auto_mapping": null,
-  "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M",
-  "bias": "none",
-  "corda_config": null,
-  "ensure_weight_tying": false,
-  "eva_config": null,
-  "exclude_modules": null,
-  "fan_in_fan_out": false,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 32,
-  "lora_bias": false,
-  "lora_dropout": 0.1,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "peft_version": "0.18.0",
-  "qalora_group_size": 16,
-  "r": 16,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "k_proj",
-    "up_proj",
-    "down_proj",
-    "o_proj",
-    "q_proj",
-    "gate_proj",
-    "v_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}

 {
+    "alpha_pattern": {},
+    "auto_mapping": null,
+    "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M",
+    "bias": "none",
+    "fan_in_fan_out": false,
+    "inference_mode": true,
+    "init_lora_weights": true,
+    "layers_pattern": null,
+    "layers_to_transform": null,
+    "loftq_config": {},
+    "lora_alpha": 32,
+    "lora_dropout": 0.1,
+    "modules_to_save": null,
+    "peft_type": "LORA",
+    "r": 16,
+    "rank_pattern": {},
+    "target_modules": [
+        "k_proj",
+        "up_proj",
+        "down_proj",
+        "o_proj",
+        "q_proj",
+        "gate_proj",
+        "v_proj"
+    ],
+    "task_type": "CAUSAL_LM",
+    "use_dora": false,
+    "use_rslora": false
+}