Instructions to use Natarizki/CMLM-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Natarizki/CMLM-0.8B with Transformers:

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

pipe = pipeline("text-generation", model="Natarizki/CMLM-0.8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Natarizki/CMLM-0.8B")
model = AutoModelForMultimodalLM.from_pretrained("Natarizki/CMLM-0.8B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Natarizki/CMLM-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Natarizki/CMLM-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Natarizki/CMLM-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Natarizki/CMLM-0.8B

SGLang

How to use Natarizki/CMLM-0.8B 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 "Natarizki/CMLM-0.8B" \
    --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": "Natarizki/CMLM-0.8B",
		"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 "Natarizki/CMLM-0.8B" \
        --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": "Natarizki/CMLM-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Natarizki/CMLM-0.8B with Docker Model Runner:
```
docker model run hf.co/Natarizki/CMLM-0.8B
```

CMLM-0.8B

Coding + Math Language Model — a Qwen3.5-0.8B fine-tuned for code generation and mathematical reasoning.

Model Details

Property	Value
Base Model	unsloth/Qwen3.5-0.8B
Architecture	Qwen3.5 (Gated DeltaNet + Full Attention hybrid)
Parameters	0.8B
Training Method	LoRA (r=16, α=32)
Precision	float32 (no quantization)
Max Context	2048 tokens
Framework	Unsloth + TRL SFTTrainer
Hardware	NVIDIA Tesla T4 (16 GB VRAM)

Training Data

Dataset	Samples	Domain
Magicoder-Evol-Instruct-110K	25,000	Code instruction following
MetaMathQA	25,000	Mathematical reasoning
NuminaMath-CoT	15,000	Math chain-of-thought
Total	65,000

Training Hyperparameters

learning_rate: 2e-4
max_steps: 500
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
effective_batch_size: 16
warmup_steps: 100
optimizer: adamw_8bit
gradient_checkpointing: unsloth
lora_r: 16
lora_alpha: 32
lora_dropout: 0
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
packing: true
max_seq_length: 2048

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Natarizki/CMLM-0.8B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Natarizki/CMLM-0.8B")

messages = [{"role": "user", "content": "Write a Python function to check if a number is prime."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmarks

Domain	CMLM-0.8B (tok/s)	Base Qwen3.5-0.8B (tok/s)	Avg Latency (CMLM)
Coding	12.1	15.2	39.0s
Math	14.8	15.1	15.9s
General	15.0	15.1	17.0s

Note: CMLM generates longer, more detailed responses for coding tasks (hence lower tok/s but higher quality). Math and general domains show near-parity with base model throughput. Benchmarked on NVIDIA T4 with float32 inference via Unsloth.

Limitations

Trained on 65K samples; may underperform on niche domains
2048 token context limit; not suitable for long-document tasks
float32 training preserves accuracy but increases inference memory vs. quantized variants
No vision capabilities despite Qwen3.5's native multimodal architecture

License

Apache 2.0 (inherits from Qwen3.5)

Acknowledgments

Qwen Team for the base model
Unsloth for efficient T4-compatible training
Dataset authors: Magicoder, MetaMath, NuminaMath teams

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Safetensors

Model size

0.9B params

Tensor type

F32

BF16

Model tree for Natarizki/CMLM-0.8B

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

unsloth/Qwen3.5-0.8B

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Adapters

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CMLM-0.8B