Instructions to use h4rz3rk4s3/TinyNewsLlama-1.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h4rz3rk4s3/TinyNewsLlama-1.1B with Transformers:

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

pipe = pipeline("text-generation", model="h4rz3rk4s3/TinyNewsLlama-1.1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("h4rz3rk4s3/TinyNewsLlama-1.1B")
model = AutoModelForCausalLM.from_pretrained("h4rz3rk4s3/TinyNewsLlama-1.1B")
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 h4rz3rk4s3/TinyNewsLlama-1.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h4rz3rk4s3/TinyNewsLlama-1.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h4rz3rk4s3/TinyNewsLlama-1.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h4rz3rk4s3/TinyNewsLlama-1.1B

SGLang

How to use h4rz3rk4s3/TinyNewsLlama-1.1B 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 "h4rz3rk4s3/TinyNewsLlama-1.1B" \
    --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": "h4rz3rk4s3/TinyNewsLlama-1.1B",
		"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 "h4rz3rk4s3/TinyNewsLlama-1.1B" \
        --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": "h4rz3rk4s3/TinyNewsLlama-1.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use h4rz3rk4s3/TinyNewsLlama-1.1B with Docker Model Runner:
```
docker model run hf.co/h4rz3rk4s3/TinyNewsLlama-1.1B
```

h4rz3rk4s3 commited on Feb 13, 2024

Commit

19afeb6

verified ·

1 Parent(s): 98ad086

Update README.md

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@@ -1,3 +1,49 @@
 ---
 license: apache-2.0
 ---

 ---
 license: apache-2.0
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+tags:
+- TinyLlama
+- QLoRA
+- Politics
+- News
+- sft
+language:
+- en
+pipeline_tag: text-generation
 ---
+# TinyNewsLlama-1.1B
+TinyNewsLlama-1.1B is a QLoRA SFT fine-tune of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using a sample of a concentrated version of the [bigNews] (https://paperswithcode.com/dataset/bignews) Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.
+The goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.
+The used training data contains political news articles from **The New York Times**, **USA Today** and **The Washington Times**. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.
+## 💻 Usage
+```python
+!pip install -qU transformers accelerate
+from transformers import AutoTokenizer
+import transformers
+import torch
+model = "h4rz3rk4s3/TinyNewsLlama-1.1B"
+messages = [
+    {
+        "role": "system",
+        "content": "You are a an experienced journalist.",
+    },
+    {"role": "user", "content": "Write a short article on Brexit and it's impact on the European Union."},
+]
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    device_map="auto",
+)
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```