Instructions to use nirajandhakal/LLaMA3-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nirajandhakal/LLaMA3-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="nirajandhakal/LLaMA3-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nirajandhakal/LLaMA3-Reasoning")
model = AutoModelForCausalLM.from_pretrained("nirajandhakal/LLaMA3-Reasoning", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nirajandhakal/LLaMA3-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nirajandhakal/LLaMA3-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nirajandhakal/LLaMA3-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nirajandhakal/LLaMA3-Reasoning

SGLang

How to use nirajandhakal/LLaMA3-Reasoning 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 "nirajandhakal/LLaMA3-Reasoning" \
    --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": "nirajandhakal/LLaMA3-Reasoning",
		"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 "nirajandhakal/LLaMA3-Reasoning" \
        --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": "nirajandhakal/LLaMA3-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nirajandhakal/LLaMA3-Reasoning with Docker Model Runner:
```
docker model run hf.co/nirajandhakal/LLaMA3-Reasoning
```

LLaMA3-Reasoning

File size: 1,232 Bytes

21d95a5
 
fabb9f4
 
 
 
 
 
21d95a5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fabb9f4
 
 
 
 
21d95a5
fabb9f4
21d95a5
380a1ba
fabb9f4
 
21d95a5
fabb9f4
 
21d95a5
 
05f15f2
21d95a5
fabb9f4
 
21d95a5
fabb9f4
 
21d95a5
fabb9f4

---
library_name: transformers
license: llama3
base_model:
- meta-llama/Meta-Llama-3-8B
tags:
- LLaMA3
- llama
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Nirajan Dhakal
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** LLaMA 3 Community License
  

Running Inference:

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("nirajandhakal/LLaMA3-Reasoning")
model = AutoModelForCausalLM.from_pretrained("nirajandhakal/LLaMA3-Reasoning")


pipe = pipeline("text-generation", model="nirajandhakal/LLaMA3-Reasoning", truncation=True)

# Define a prompt for the model
prompt = "What are the benefits of using artificial intelligence in healthcare?"

# Generate text based on the prompt
generated_text = pipe(prompt, max_length=200)

# Print the generated text
print(generated_text[0]['generated_text'])
```