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")

# 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 / tokenizer_config.json

jaweed123

learnllm-100m-instruct: SFT model (response-masked attention + label fix)

ba571d6 verified 19 days ago

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519 Bytes

	{
	"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"
	}
	}