gemma-3-4b-it-together-inference-faq

Gemma 3 4B IT - Together AI Inference FAQ

Fine-tuned version of google/gemma-3-4b-it on Together AI inference FAQ data.

Training Details

• Base model: google/gemma-3-4b-it
• Method: LoRA (r=64, alpha=128)
• Epochs: 3
• Learning rate: 1e-5
• Training data: 35 Q&A pairs about Together AI inference platform
• Fine-tuned on: Together AI fine-tuning platform
• Job ID: ft-15b4f6f0-6a8d
• Together output name: ighutake_21a0/gemma-3-4b-it-together-inference-faq-123c2a4f

Usage

Via Together AI API

from together import Together
client = Together()

response = client.chat.completions.create(
    model="ighutake_21a0/gemma-3-4b-it-together-inference-faq-123c2a4f",
    messages=[{"role": "user", "content": "How do I use streaming with Together AI?"}],
    max_tokens=512
)
print(response.choices[0].message.content)

Via Hugging Face Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/gemma-3-4b-it-together-inference-faq")
model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/gemma-3-4b-it-together-inference-faq")

Topics Covered

• API endpoints and authentication
• Chat completions parameters
• Streaming responses
• JSON mode / structured outputs
• Function calling
• Rate limits and error handling
• Dedicated endpoints vs serverless
• Model pricing
• Fine-tuning workflows
• Batch inference
• Vision and multimodal capabilities
• Audio/speech features
• Prompt caching and optimization
• Speculative decoding
• GPU cloud options
• And more...