Instructions to use Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct")
model = AutoModelForCausalLM.from_pretrained("Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Doctor-Shotgun/TinyLlama-1.1B-32k-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": "Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct

SGLang

How to use Doctor-Shotgun/TinyLlama-1.1B-32k-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 "Doctor-Shotgun/TinyLlama-1.1B-32k-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": "Doctor-Shotgun/TinyLlama-1.1B-32k-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 "Doctor-Shotgun/TinyLlama-1.1B-32k-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": "Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct with Docker Model Runner:
```
docker model run hf.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Norobara-ZLoss-8x7B

This is an instruct-tuned TinyLlama-1.1B-32k on several open-source instruct datasets, intended primarily for speculative decoding.

Usage:

The intended prompt format is a modified multi-turn Alpaca instruction format:

### Instruction:
{system prompt}

### Input:
{user message}

### Response:
{model response}

### Input:
{user message}

### Response:
{model response}

(etc.)

Bias, Risks, and Limitations

The model will show biases present in the base model. No ethical alignment was applied to prevent the generation of toxic or harmful outputs (in fact the opposite, with examples from toxic-DPO included), so generate at your own risk.