Instructions to use arirajuns/TinyLlama-1.1B-Slerp-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arirajuns/TinyLlama-1.1B-Slerp-v1 with Transformers:

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

pipe = pipeline("text-generation", model="arirajuns/TinyLlama-1.1B-Slerp-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arirajuns/TinyLlama-1.1B-Slerp-v1")
model = AutoModelForCausalLM.from_pretrained("arirajuns/TinyLlama-1.1B-Slerp-v1")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arirajuns/TinyLlama-1.1B-Slerp-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arirajuns/TinyLlama-1.1B-Slerp-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arirajuns/TinyLlama-1.1B-Slerp-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arirajuns/TinyLlama-1.1B-Slerp-v1

SGLang

How to use arirajuns/TinyLlama-1.1B-Slerp-v1 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 "arirajuns/TinyLlama-1.1B-Slerp-v1" \
    --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": "arirajuns/TinyLlama-1.1B-Slerp-v1",
		"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 "arirajuns/TinyLlama-1.1B-Slerp-v1" \
        --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": "arirajuns/TinyLlama-1.1B-Slerp-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arirajuns/TinyLlama-1.1B-Slerp-v1 with Docker Model Runner:
```
docker model run hf.co/arirajuns/TinyLlama-1.1B-Slerp-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TinyLlama-1.1B-Slerp-v1

This model is a merge of pre-trained language models created using mergekit. It combines the robustness of an intermediate checkpoint with the instructional capabilities of the Chat version, resulting in a model that balances foundational knowledge with instruction following.

⚡ Merge Details

This merge uses the SLERP (Spherical Linear Interpolation) method, which interpolates between the weights of the two models on a hypersphere. This method is generally preferred over simple linear averaging (soups) as it better preserves the geometric properties of the original models' feature spaces.

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: slerp
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
parameters:
  t:
    - value: 0.5
dtype: float16
slices:
  - sources:
      - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
        layer_range: [0, 22]
      - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
        layer_range: [0, 22]
        parameters:
          t:
            - value: 0.5

## 💻 Usage

You can use this model easily with the `pipeline` API, which handles the chat templates automatically.

```python
import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="arirajuns/TinyLlama-1.1B-Slerp-v1",
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of 'Model Merging' in one sentence."},
]

prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

outputs = pipe(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for arirajuns/TinyLlama-1.1B-Slerp-v1

TinyLlama/TinyLlama-1.1B-Chat-v1.0

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Merge model

this model

Quantizations

1 model