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 / TRAINING_REPORT.md

mossez-systems

Publish Mossez-100M-Coder-Instruct

8dc5e05 verified 11 days ago

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	# Training report

	## Lineage

	- Source weights: the selected one-epoch Mossez-100M-Coder-Base.
	- Source model SHA-256: `aba529bf10ad9f3acb5294c8bc2b4c93d20d25c6cff3a235a8659503b9ac1837`.
	- Final model SHA-256: `0aade7d070122633abccd70cc5500e5bc36c7f69fafeadd9f5ee0b5a3e0766bf`.
	- The general Mossez-100M-Instruct supplied no source weights.

	## Run

	- Objective: assistant-only supervised fine-tuning.
	- One bounded epoch: 660 contiguous finite optimizer steps.
	- Examples: 2,640 train; 330 validation; 330 test.
	- Rendered training tokens: 323,960; assistant target tokens: 105,100.
	- Precision: BF16; fused AdamW; micro-batch 1; gradient accumulation 4.
	- Validation loss: 1.991049 at step 0 to 0.031888 at step 660.
	- Early stopping: not triggered; validation regression count: 0.
	- Final checkpoint: best validation checkpoint, step 660.

	## Preflight and integrity

	- Assistant-only masks were asserted at corpus load and before each forward pass.
	- A real CUDA memory probe passed through micro-batch 8; training used micro-batch 1.
	- A 10-step smoke passed, followed by a bit-exact cross-process resume test.
	- An independent 100-step pilot improved overall and all per-task held-out losses.
	- Corpus manifest SHA-256: `aca68b911788a1a7d93671bc27835b3d279cd612c02e8e3add91e2d3b2079084`.
	- Tokenizer manifest SHA-256: `f24c2e10522e866a889177e3a2258d84f1d8a863d784894a72974e391c1abdec`.