Ornith-1.0-35B-GGUF vs dolphin-2.9.1-yi-1.5-34b
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
dphn/dolphin-2.9.1-yi-1.5-34b
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | Ornith-1.0-35B-GGUF | dolphin-2.9.1-yi-1.5-34b | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 35.00B | 34.00B | +1B (+2.9%) |
| Model family | Other | Llama | Different |
Performance Verdict
Based on the available leaderboard data, deepreinforce-ai/Ornith-1.0-35B-GGUF has the stronger overall benchmark score.
- deepreinforce-ai/Ornith-1.0-35B-GGUF is the stronger performer, scoring 98.50 on average compared to dphn/dolphin-2.9.1-yi-1.5-34b's 98.50.
- deepreinforce-ai/Ornith-1.0-35B-GGUF is 2.9% larger in parameter capacity than dphn/dolphin-2.9.1-yi-1.5-34b (35.00B vs 34.00B parameters).
- deepreinforce-ai/Ornith-1.0-35B-GGUF has more parameter capacity, which may contribute to its stronger benchmark score.
Integration & Implementation Guide
Learn how to load and execute these models programmatically in Python using Hugging Face's transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("deepreinforce-ai/Ornith-1.0-35B-GGUF")
model = AutoModelForCausalLM.from_pretrained("deepreinforce-ai/Ornith-1.0-35B-GGUF")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9.1-yi-1.5-34b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9.1-yi-1.5-34b")
Compare Alternative Models
Explore nearby pairings from the same model dataset.
Need This In Production?
I can help with model hosting, quantization, API integration, RAG systems, and production rollout.