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 / DATASET_ATTRIBUTION.md

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Dataset attribution

The Coder-Instruct v1 SFT corpus is deterministic project-authored material licensed under Apache-2.0. It imports no external instruction dataset, private data, chat exports, Telegram data, or chain-of-thought material.

Composition

Train: 2,640 examples, 323,960 rendered tokens, 88 source groups.
Validation: 330 examples, 40,504 rendered tokens, 11 source groups.
Test: 330 examples, 40,518 rendered tokens, 11 source groups.
Maximum rendered sequence: 149 tokens; model limit: 1,024.
Eleven balanced task types: short function, completion, explanation, bug fix, refactor, unit test, traceback, JSON/YAML conversion, SQL, PowerShell, and FIM repair.
Source groups are disjoint across train, validation, and test.

Gates

License/provenance, secret, email-like PII, exact deduplication, cross-split source-group leakage, chain-of-thought exclusion, and chat-marker collision gates passed. Bounded local validators passed for Python AST/fragments, JSON, YAML, SQL, safe PowerShell, and concise text.

This conservative, template-heavy corpus validates the SFT pipeline and balanced held-out evaluation. It does not establish broad coding-assistant competence.