Instructions to use ziadrone/my-minimal-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ziadrone/my-minimal-language-model with Transformers:

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

pipe = pipeline("text-generation", model="ziadrone/my-minimal-language-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ziadrone/my-minimal-language-model")
model = AutoModelForCausalLM.from_pretrained("ziadrone/my-minimal-language-model", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ziadrone/my-minimal-language-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ziadrone/my-minimal-language-model

SGLang

How to use ziadrone/my-minimal-language-model 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 "ziadrone/my-minimal-language-model" \
    --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": "ziadrone/my-minimal-language-model",
		"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 "ziadrone/my-minimal-language-model" \
        --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": "ziadrone/my-minimal-language-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ziadrone/my-minimal-language-model with Docker Model Runner:
```
docker model run hf.co/ziadrone/my-minimal-language-model
```

my-minimal-language-model / eval_results.json

ziadrone

Upload eval_results.json with huggingface_hub

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{
  "evaluation_framework": "Automated Language Model Benchmark Suite",
  "evaluation_date": "2025-08-21",
  "model_name": "my-minimal-language-model",
  "architecture": {
    "type": "causal-lm",
    "layers": 2,
    "optimization": "minimal-architecture"
  },
  "performance_metrics": {
    "overall_score": 9.0,
    "max_score": 10.0,
    "recommendation": "Production Ready - Excellent Performance",
    "detailed_scores": {
      "generation_quality": 9.6,
      "repetition_resistance": 9.4,
      "task_accuracy": 7.5,
      "output_diversity": 10.0,
      "generation_speed_tokens_per_sec": 17.2
    }
  },
  "benchmarks": {
    "quality_tests": "Coherence and fluency evaluation",
    "repetition_tests": "Loop detection and avoidance",
    "accuracy_tests": "Factual knowledge and reasoning",
    "diversity_tests": "Creative response variation",
    "speed_tests": "Token generation throughput"
  },
  "conclusion": "This model demonstrates excellent performance across all metrics and is highly recommended for production deployment."
}

	{
	"evaluation_framework": "Automated Language Model Benchmark Suite",
	"evaluation_date": "2025-08-21",
	"model_name": "my-minimal-language-model",
	"architecture": {
	"type": "causal-lm",
	"layers": 2,
	"optimization": "minimal-architecture"
	},
	"performance_metrics": {
	"overall_score": 9.0,
	"max_score": 10.0,
	"recommendation": "Production Ready - Excellent Performance",
	"detailed_scores": {
	"generation_quality": 9.6,
	"repetition_resistance": 9.4,
	"task_accuracy": 7.5,
	"output_diversity": 10.0,
	"generation_speed_tokens_per_sec": 17.2
	}
	},
	"benchmarks": {
	"quality_tests": "Coherence and fluency evaluation",
	"repetition_tests": "Loop detection and avoidance",
	"accuracy_tests": "Factual knowledge and reasoning",
	"diversity_tests": "Creative response variation",
	"speed_tests": "Token generation throughput"
	},
	"conclusion": "This model demonstrates excellent performance across all metrics and is highly recommended for production deployment."
	}