---
license: apache-2.0
base_model: unsloth/Qwen3.5-0.8B
tags:
  - code-generation
  - math-reasoning
  - qwen3.5
  - lora
  - sft
datasets:
  - ise-uiuc/Magicoder-Evol-Instruct-110K
  - meta-math/MetaMathQA
  - AI-MO/NuminaMath-CoT
library_name: transformers
pipeline_tag: text-generation
---

# CMLM-0.8B

**C**oding + **M**ath **L**anguage **M**odel — a Qwen3.5-0.8B fine-tuned for code generation and mathematical reasoning.

## Model Details

| Property | Value |
| :--- | :--- |
| Base Model | [unsloth/Qwen3.5-0.8B](https://huggingface.co/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](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K) | 25,000 | Code instruction following |
| [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) | 25,000 | Mathematical reasoning |
| [NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | 15,000 | Math chain-of-thought |
| **Total** | **65,000** | |

## Training Hyperparameters

```yaml
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

```python
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](https://huggingface.co/Qwen) for the base model
- [Unsloth](https://unsloth.ai/) for efficient T4-compatible training
- Dataset authors: Magicoder, MetaMath, NuminaMath teams