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 / README.md

mossez-systems

Publish Mossez-100M-Coder-Instruct

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model:
	- mossez-systems/Mossez-100M-Coder-Base
	tags:
	- causal-lm
	- conversational
	- code
	- fill-in-the-middle
	- instruct
	- llama
	- research
	- experimental
	---

	# Mossez-100M-Coder-Instruct

	Mossez-100M-Coder-Instruct is an experimental 100M-parameter coding instruction
	model with this weight lineage:

	`Mossez-100M-Base -> Mossez-100M-Coder-Base -> Mossez-100M-Coder-Instruct`.

	The general [`Mossez-100M-Instruct`](https://huggingface.co/mossez-systems/Mossez-100M-Instruct)
	was used only as a tokenizer, chat-template, release, and inference reference;
	its weights were not used as source weights for this model.

	## Model details

	\| Property \| Value \|
	\|---\|---:\|
	\| Parameters \| 100,098,048 \|
	\| Architecture \| Llama-compatible decoder-only Transformer \|
	\| Layers / hidden size \| 12 / 768 \|
	\| Query / KV heads \| 12 / 4 \|
	\| Context length \| 1,024 tokens \|
	\| Vocabulary \| 32,007 \|
	\| Objective \| Assistant-only SFT loss \|
	\| Weight format \| Safetensors, FP32 \|
	\| License \| Apache-2.0 \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "mossez-systems/Mossez-100M-Coder-Instruct"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

	messages = [{"role": "user", "content": "Write a short Python function that adds two integers."}]
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	output = model.generate(**inputs, do_sample=False, max_new_tokens=96)
	new_tokens = output[0, inputs.input_ids.shape[1]:]
	print(tokenizer.decode(new_tokens, skip_special_tokens=True))
	```

	## Training and evaluation

	The model was fine-tuned for one bounded epoch: 660 optimizer steps over 2,640
	project-authored examples, using assistant-only loss. Immutable validation and
	test sets contain 330 examples each across 11 balanced task types. See
	[TRAINING_REPORT.md](TRAINING_REPORT.md), [EVALUATION.md](EVALUATION.md), and
	[DATASET_ATTRIBUTION.md](DATASET_ATTRIBUTION.md).

	The released `model.safetensors` SHA-256 is
	`0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf`.

	## Limitations

	This is a small research model, not a reliable or safe production coding
	assistant. The authored SFT corpus is balanced but narrow and template-heavy,
	so held-out loss may overstate general-world capability. Expect repetition,
	incorrect constants, malformed code, hallucinated APIs, weak instruction
	following, and early EOS. Validate, test, and sandbox every output.