Instructions to use jaweed123/TinyJLLM-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaweed123/TinyJLLM-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="jaweed123/TinyJLLM-Instruct")

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

tokenizer = AutoTokenizer.from_pretrained("jaweed123/TinyJLLM-Instruct")
model = AutoModelForCausalLM.from_pretrained("jaweed123/TinyJLLM-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaweed123/TinyJLLM-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaweed123/TinyJLLM-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaweed123/TinyJLLM-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jaweed123/TinyJLLM-Instruct

SGLang

How to use jaweed123/TinyJLLM-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 "jaweed123/TinyJLLM-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaweed123/TinyJLLM-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "jaweed123/TinyJLLM-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaweed123/TinyJLLM-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jaweed123/TinyJLLM-Instruct with Docker Model Runner:
```
docker model run hf.co/jaweed123/TinyJLLM-Instruct
```

TinyJLLM-Instruct

File size: 519 Bytes

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{
  "tokenizer_class": "PreTrainedTokenizerFast",
  "bos_token": "<bos>",
  "eos_token": "<eos>",
  "unk_token": "<unk>",
  "pad_token": "<pad>",
  "model_max_length": 512,
  "vocab_size": 32000,
  "clean_up_tokenization_spaces": true,
  "_learnllm_meta": {
    "vocab_size": 32000,
    "min_frequency": 2,
    "special_tokens": [
      "<pad>",
      "<unk>",
      "<bos>",
      "<eos>"
    ],
    "sample_size_mb": 250.0,
    "seed": 42,
    "save_dir": "J:\\LearnLLM\\models\\tokenizer"
  }
}