Instructions to use mossez-systems/Mossez-100M-Coder-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mossez-systems/Mossez-100M-Coder-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mossez-systems/Mossez-100M-Coder-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mossez-systems/Mossez-100M-Coder-Instruct")
model = AutoModelForCausalLM.from_pretrained("mossez-systems/Mossez-100M-Coder-Instruct", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mossez-systems/Mossez-100M-Coder-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mossez-systems/Mossez-100M-Coder-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Coder-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mossez-systems/Mossez-100M-Coder-Instruct

SGLang

How to use mossez-systems/Mossez-100M-Coder-Instruct 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 "mossez-systems/Mossez-100M-Coder-Instruct" \
    --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": "mossez-systems/Mossez-100M-Coder-Instruct",
		"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 "mossez-systems/Mossez-100M-Coder-Instruct" \
        --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": "mossez-systems/Mossez-100M-Coder-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mossez-systems/Mossez-100M-Coder-Instruct with Docker Model Runner:
```
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Instruct
```

Mossez-100M-Coder-Instruct

File size: 1,477 Bytes

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# Evaluation

Evaluation used frozen, source-group-disjoint validation and test splits with
assistant-only loss. Lower is better.

| Model | Validation loss | Test loss |
|---|---:|---:|
| Source Coder-Base | 2.008803 | 2.023115 |
| One-epoch Coder-Instruct | 0.029855 | 0.032257 |

## Per-task loss

| Task | Source val | Candidate val | Source test | Candidate test |
|---|---:|---:|---:|---:|
| bug_fix | 0.666634 | 0.002418 | 0.685901 | 0.002504 |
| completion | 1.098412 | 0.072219 | 1.184431 | 0.074582 |
| explain | 3.757432 | 0.003248 | 3.861036 | 0.003465 |
| fim_repair | 2.926159 | 0.171918 | 2.934412 | 0.188182 |
| json_yaml_conversion | 1.304223 | 0.002704 | 1.310993 | 0.002706 |
| refactor | 1.088105 | 0.002698 | 1.089293 | 0.002557 |
| shell | 2.545998 | 0.001775 | 2.548895 | 0.001855 |
| short_function | 1.503674 | 0.003699 | 1.520041 | 0.003951 |
| sql | 2.761549 | 0.018859 | 2.715693 | 0.021215 |
| traceback | 3.734223 | 0.002768 | 3.769516 | 0.002884 |
| unit_test | 1.751150 | 0.075700 | 1.721415 | 0.083690 |

Overall and all 11 per-task losses improved against both the source and the
100-step SFT pilot. A deterministic 11-prompt generation smoke passed all
local syntax/format/safety validators; 9 outputs exactly matched references.

These results come from a small, project-authored, template-heavy corpus.
They do not establish performance on HumanEval, MBPP, SWE-bench, security
tasks, repository-level work, or arbitrary real-world prompts.