Instructions to use prithivMLmods/Llama3.2-1B-Grpo-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Llama3.2-1B-Grpo-Exp with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Llama3.2-1B-Grpo-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Llama3.2-1B-Grpo-Exp")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Llama3.2-1B-Grpo-Exp")
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 Settings

vLLM

How to use prithivMLmods/Llama3.2-1B-Grpo-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Llama3.2-1B-Grpo-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama3.2-1B-Grpo-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Llama3.2-1B-Grpo-Exp

SGLang

How to use prithivMLmods/Llama3.2-1B-Grpo-Exp 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 "prithivMLmods/Llama3.2-1B-Grpo-Exp" \
    --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": "prithivMLmods/Llama3.2-1B-Grpo-Exp",
		"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 "prithivMLmods/Llama3.2-1B-Grpo-Exp" \
        --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": "prithivMLmods/Llama3.2-1B-Grpo-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Llama3.2-1B-Grpo-Exp with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama3.2-1B-Grpo-Exp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3.2-1B-Grpo-Exp

The Llama3.2-1B-Grpo-Exp is a fine-tuned version of the Llama-3.1-8B base model, further enhanced with the GSM8K dataset for superior text generation and mathematical reasoning. This model is designed for advanced reasoning, structured problem-solving, and contextually rich outputs, making it an excellent choice for applications in education, programming, research, and creative writing.

With its optimized architecture, Llama3.2-1B-Grpo-Exp excels at:

Logical reasoning and step-by-step problem-solving
Mathematical and coding tasks, leveraging specialized expert models
Generating long-form content (up to 8K tokens) with improved coherence
Understanding structured data, including tables and JSON outputs
Following instructions and adapting to diverse system prompts, making it ideal for chatbots and AI assistants

Key Features

Supports long-context processing of up to 128K tokens
Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)

Model Architecture

Llama3.2-1B-Grpo-Exp is built on the optimized transformer architecture of Llama-3.1-8B, integrating enhanced dataset logits from GSM8K for better mathematical reasoning and structured output generation.

Using with transformers

To run conversational inference using transformers >= 4.43.0, use the pipeline abstraction or leverage the generate() function with the Auto classes.

Ensure your environment is updated with:

pip install --upgrade transformers

Example Usage

import torch  
from transformers import pipeline  

model_id = "prithivMLmods/Llama3.2-1B-Grpo-Exp"  
pipe = pipeline(  
    "text-generation",  
    model=model_id,  
    torch_dtype=torch.bfloat16,  
    device_map="auto",  
)  

messages = [  
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},  
    {"role": "user", "content": "Who are you?"},  
]  

outputs = pipe(  
    messages,  
    max_new_tokens=256,  
)  
print(outputs[0]["generated_text"][-1])

Intended Use

Llama3.2-1B-Grpo-Exp is designed for a wide range of applications requiring deep reasoning, structured outputs, and logical text generation. It is particularly suited for:

Education & Research: Generating detailed explanations, step-by-step solutions, and structured academic content.
Programming & Code Generation: Assisting in code writing, debugging, and algorithm explanations with improved logic structuring.
AI Chatbots & Assistants: Providing context-aware, instruction-following responses for conversational AI applications.
Creative Writing: Generating high-quality stories, articles, and structured narratives with coherence.
Data Analysis & Structured Output Generation: Interpreting and generating JSON, tables, and formatted outputs for structured data processing.

Limitations

While Llama3.2-1B-Grpo-Exp is optimized for deep reasoning and structured outputs, it has some limitations:

Not a Real-time Knowledge Source
- The model is trained on a fixed dataset and does not have real-time internet access. It may not provide up-to-date information on rapidly evolving topics.
Potential Biases
- As with all AI models, responses may reflect biases present in the training data. Users should critically evaluate outputs, especially in sensitive domains.
Mathematical & Logical Reasoning Constraints
- While strong in step-by-step reasoning, it may occasionally produce incorrect mathematical calculations or logical inconsistencies. External verification is recommended for critical applications.
Handling of Extremely Long Contexts
- While it supports up to 128K tokens, efficiency and coherence may degrade when processing very long documents or conversations.
Limited Handling of Ambiguity
- The model may struggle with highly ambiguous or context-dependent queries, sometimes generating plausible but incorrect responses.
Ethical & Compliance Considerations
- Not intended for generating misinformation, automating legal or medical decisions, or other high-risk applications without human oversight.

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