Development of Humanlike Chat Fine-tune for Qwen3.8-27B
A new fine-tuned version of the Qwen3.8-27B large language model, titled “Qwen3.8-27B-Humanlike-Chat,” has been released by an independent developer. According to reports on Reddit, the model was specifically designed to move away from the standard “AI assistant” persona—characterized by verbosity, excessive politeness, and constant explanations—in favor of a conversational style that more closely mimics human-to-human interaction.
Training Methodology and Dataset
The developer utilized a rank-256 LoRA (Low-Rank Adaptation) trained on top of the huihui-ai/Huihui-Qwen3.8-27B-abliterated base model. The training process relied on a dataset consisting of 125,217 obfuscated human-to-human messages sourced from 1,396 distinct chat conversations.
The stated objective of the fine-tuning was not to increase the model’s intelligence or improve benchmark performance, but to alter its conversational habits. The developer noted that the resulting model produces shorter, less polished replies that avoid the tendency to turn every user input into a formal explanation.
Performance and Benchmarks
The release of this specific checkpoint (version 863) carries potential trade-offs regarding instruction following. The developer reported that an earlier iteration of the model scored five percentage points lower than its parent model on IFEval, a benchmark used to measure how well a model follows specific instructions. However, the developer noted that this specific benchmark has not been rerun on the current checkpoint, and coding performance remains untested.
The model is currently available via Hugging Face in GGUF format and as a standalone F32 LoRA adapter. A demo is available via Hugging Face Spaces, and a rate-limited, OpenAI-compatible endpoint has been provided for testing.
Technical Implementation and Hardware Requirements
In community discussions on Reddit, users have explored the deployment of Qwen3.8-27B models on high-end consumer hardware. One user reported achieving generation speeds of 70-100 tokens per second (t/s) using a system equipped with an NVIDIA RTX 5090 and 64GB of RAM, utilizing llama.cpp with a 130,000 context window.
The technical setup discussed included the use of llama-server with specific parameters such as --n-gpu-layers 99 and --spec-type draft-mtp to optimize performance. Users noted that while higher quantization levels (such as Q8) are possible, they may limit the available context size for agentic use cases, leading some to prefer Q5 or Q6 quantizations.