// the find
PeterH0323/Streamer-Sales
Streamer-Sales 销冠 —— 卖货主播 LLM 大模型🛒🎁，一个能够根据给定的商品特点从激发用户购买意愿角度出发进行商品解说的卖货主播大模型。🚀⭐内含详细的数据生成流程❗ 📦另外还集成了 LMDeploy 加速推理🚀、RAG检索增强生成 📚、TTS文字转语音🔊、数字人生成 🦸、 Agent 使用网络查询实时信息🌐、ASR 语音转文字🎙️、Vue 生态搭建前端🍍、FastAPI 搭建后端🗝️、Docker-compose 打包部署🐋
Streamer-Sales is a fine-tuned InternLM2-7B model trained to play the role of a Chinese live-stream shopping host — it generates sales scripts, answers buyer questions in character, and supports RAG against product manuals. The full system layers on TTS (GPT-SoVITS), ASR (FunASR), a talking-head digital human (MuseTalk), and a FastAPI/Vue admin backend. It's aimed at Chinese-market e-commerce teams or researchers studying conversational sales AI.
The data pipeline is unusually well-documented: the YAML-driven dataset generator, the OCR-to-manual pipeline, and the published training JSONL with ~1500 conversations let you reproduce or extend the fine-tune without reverse-engineering anything. LMDeploy TurboMind integration is a real win — the benchmark shows 5x throughput improvement with 4-bit quantization, dropping from 61 to 307 words/s, which matters a lot for real-time streaming. The microservice decomposition (LLM, TTS, ASR, digital human as separate FastAPI services behind one middleware) is sensible for production: you can scale or swap each independently. RAG via product manuals means you can onboard new products without retraining — just upload a manual and rebuild the vector store.
The whole stack is China-specific and largely undocumented in English, making it a hard adopt for anyone outside that context — the model, the training data, the TTS voices, and the Agent APIs (delivery tracking, weather) are all tuned for Chinese. The digital human generation isn't real-time: it uses ComfyUI workflows and MuseTalk to produce video clips offline, not a live avatar, which is a significant gap versus the README's framing. The only supported base model is InternLM2-chat-7b, and the fine-tuning configs are hardcoded to it — switching to Qwen2 or LLaMA3 requires non-trivial work with no guidance. Docker Compose support is backend-only (the README says so in two places), meaning the Vue frontend still requires manual Node setup, which undermines the 'one-click deploy' claim.