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 / generation_config.json

jaweed123

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

ba571d6 verified about 2 months ago

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{
  "max_new_tokens": 100,
  "temperature": 0.8,
  "top_k": 50,
  "top_p": 0.95,
  "bos_token_id": 2,
  "eos_token_id": 3,
  "pad_token_id": 0,
  "model_type": "llama",
  "_learnllm_note": "decoding defaults used by learnllm inference"
}

	{
	"max_new_tokens": 100,
	"temperature": 0.8,
	"top_k": 50,
	"top_p": 0.95,
	"bos_token_id": 2,
	"eos_token_id": 3,
	"pad_token_id": 0,
	"model_type": "llama",
	"_learnllm_note": "decoding defaults used by learnllm inference"
	}