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: 2,662 Bytes

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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.