dolphin-2.9.1-yi-1.5-34b vs Qwen3.8-27B-Uncensored-GGUF
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
JonathanColetti/Qwen3.8-27B-Uncensored-GGUF
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | dolphin-2.9.1-yi-1.5-34b | Qwen3.8-27B-Uncensored-GGUF | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 34.00B | 27.00B | +7B (+25.9%) |
| Model family | Llama | Qwen | Different |
Performance Verdict
Based on the available leaderboard data, dphn/dolphin-2.9.1-yi-1.5-34b has the stronger overall benchmark score.
- dphn/dolphin-2.9.1-yi-1.5-34b is the stronger performer, scoring 98.50 on average compared to JonathanColetti/Qwen3.8-27B-Uncensored-GGUF's 98.50.
- dphn/dolphin-2.9.1-yi-1.5-34b is 25.9% larger in parameter capacity than JonathanColetti/Qwen3.8-27B-Uncensored-GGUF (34.00B vs 27.00B parameters).
- dphn/dolphin-2.9.1-yi-1.5-34b 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, JavaScript/TypeScript, Go, Rust, C++, and PHP.
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")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("JonathanColetti/Qwen3.8-27B-Uncensored-GGUF")
model = AutoModelForCausalLM.from_pretrained("JonathanColetti/Qwen3.8-27B-Uncensored-GGUF")
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