--- license: apache-2.0 language: - en - zh tags: - unsloth - QiMing - vllm - sales - b2b - saas - fine-tuned - instruction-following - role-playing - cognitive-simulator pipeline_tag: text-generation model_name: QiMing-Sales-20B library_name: transformers base_model: - openai/gpt-oss-20b --- --- # QiMing --- ## An AI that rewrites its own rules for greater intelligence. ## 结果 (Result) = 模型内容 (Model Content) × 数学的平方 (Math²) --- **"Logic is the soul of a model, for it defines:** * **How it learns from data (The Power of Induction);** * **How it reasons and decides (The Power of Deduction);** * **Its capacity to align with human values (The Ethical Boundary);** * **Its potential to adapt to future challenges (The Evolutionary Potential).** **If a model pursues nothing but sheer scale or computational power, ignoring the depth and breadth of its logic, it risks becoming a "paper tiger"—imposing on the surface, yet hollow at its core. Conversely, a model built upon elegant logic, even with fewer parameters, can unleash its true vitality in our complex world."** --- # DISCLAIMER ## The content generated by this model is for reference purposes only. Users are advised to verify its accuracy independently before use. ## This is a 20-billion-parameter foundation model (20B). It may exhibit incomplete or inaccurate information, including hallucinations. ## If you find this AI too human-like, please remember: it is merely a more intelligent model — not an actual person. --- ### Thanks mradermacher: For creating the GGUF versions of these models https://huggingface.co/mradermacher/QiMing-Sales-20B-MXFP4-GGUF https://huggingface.co/mradermacher/QiMing-Sales-20B-MXFP4-i1-GGUF ### For developing the foundational model (aifeifei798/QiMing-Sales-20B-MXFP4) used in this project. https://huggingface.co/openai ### unsloth.ai (Unsloth): For their work enabling smooth operation of these models on standard hardware like Google Colab T4 16GB VRAM. https://unsloth.ai ### Thank Google Colab T4 16G --- # QiMing-Sales-20B-MXFP4 ## Model Description **QiMing-Sales-20B-MXFP4** is not just a sales chatbot; it is a sophisticated **Cognitive Simulator** designed for B2B sales expertise. Fine-tuned from the powerful `gpt-oss-20B` foundational model, QiMing-Sales-20B-MXFP4 has been meticulously trained on a proprietary, synthetically generated dataset that embodies the core principles of modern sales science. The model's key innovation lies in its architecture, which functions as a **"Model Capability Control Layer"**. This allows it to dynamically adopt different professional personas—from a junior **Intern** to a strategic **CEO**—and apply specific, context-aware sales logic to a given situation. Its core capabilities are structured around distinct **"Logic Modules"**, enabling it to: - **Diagnosis & Amplification Logic (Pain -> Impact)**: Diagnose a client's surface-level pain and connect it to deep, strategic business impacts. - **Solution Matching Logic (Feature -> Value)**: Translate abstract technical features into tangible, quantifiable business value. - **Objection Handling Logic (Objection -> Reframe)**: Reframe customer objections into opportunities for deeper value discussions. - **Action Guidance Logic (Status -> Next Step)**: Propose clear, actionable next steps to maintain deal momentum. - **Comparison & Selection Logic (Comparison -> Recommendation)**: Provide consultative advice by comparing solutions based on the client's core needs. - **Strategic Synthesis Logic (Strategic Synthesis)**: Combine multiple logic modules to address complex, high-level strategic challenges from decision-makers. This allows QiMing-Sales-20B-MXFP4 to go beyond generic advice, providing responses that are contextually-aware, role-appropriate, and strategically-sound, making it a powerful co-pilot for sales professionals at all levels. ## Intended Uses - **Sales Training & Role-Playing**: Simulate various customer interactions for training new and experienced sales staff. - **Sales Call & Email Generation**: Assist in drafting scripts, emails, and proposals that are logically sound and persuasive. - **Strategic Deal Coaching**: Act as a sparring partner for sales leaders to brainstorm strategies for complex deals. - **Sales Enablement Content Creation**: Generate high-quality content, such as case studies and value propositions. ## Limitations and Ethical Considerations - **Knowledge Cutoff**: The model's knowledge is based on its training data and does not have access to real-time information. - **Potential for Hallucination**: Like all LLMs, QiMing-Sales-20B-MXFP4 can occasionally generate plausible but incorrect information ("hallucinate"). All outputs, especially quantitative metrics, should be verified by a human expert. - **Bias**: The model's "expert" persona is defined by its training data, which reflects a specific B2B sales philosophy. This perspective may not be universally applicable to all industries or cultures. - **Not a Replacement for Human Judgment**: The model is intended to be a co-pilot, not an autonomous agent. Final sales decisions and client communications should always be reviewed and owned by a human professional. ## Training Procedure ### Training Data QiMing-Sales-20B-MXFP4 was **not** trained on scraped web data. It was fine-tuned on a dataset of approximately 500 high-quality, structured JSON examples, synthetically generated by a proprietary, Python-based **"Meta-Prompt Factory"**. This generator systematically combines elements from three core components: 1. **SALES_WORLDS**: A knowledge base defining realistic B2B scenarios across industries like SaaS, High-End Manufacturing, and Professional Services. 2. **ROLE_HIERARCHY**: A framework that maps sales roles (from Intern to CEO) to the specific logic modules they are expected to master. 3. **LOGIC_MODULES**: A library of core sales reasoning patterns (e.g., Pain Diagnosis, Objection Handling). Each data point consists of a structured `instruction`, `input`, and an ideal `output`, guided by a gold-standard `few-shot example`. This methodology ensures that the model learns not just to mimic language, but to internalize and apply complex, role-specific reasoning frameworks. ### Training Dataset - https://huggingface.co/datasets/aifeifei798/sales_logic ### Fine-tuning The model was fine-tuned from the `gpt-oss-20B` base model using the generated instruction-following dataset. The training process focused on teaching the model to accurately interpret the "Model Capability Control Layer" (`instruction`), apply the specified role and logic to the provided context (`input`), and generate a high-quality, structured response (`output`). # Highlights * **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. * **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs. * **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users. * **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning. * **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs. * **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `QiMing-Sales-20B-MXFP4` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization. # Inference examples ## Transformers You can use `QiMing-Sales-20B-MXFP4` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers kernels torch ``` Once, setup you can proceed to run the model by running the snippet below: ```py from transformers import pipeline import torch model_id = "aifeifei798/QiMing-Sales-20B-MXFP4" pipe = pipeline( "text-generation", model=model_id, torch_dtype="auto", device_map="auto", ) messages = [ {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver: ``` transformers serve transformers chat localhost:8000 --model-name-or-path aifeifei798/QiMing-Sales-20B-MXFP4 ``` [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers) ## vLLM vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server. ```bash uv pip install --pre vllm==0.10.1+gptoss \ --extra-index-url https://wheels.vllm.ai/gpt-oss/ \ --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \ --index-strategy unsafe-best-match vllm serve aifeifei798/QiMing-Sales-20B-MXFP4 ``` [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm) ## PyTorch / Triton To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation). #### LM Studio If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download. ```bash # QiMing-Sales-20B-MXFP4 lms get aifeifei798/QiMing-Sales-20B-MXFP4 ``` Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners. --- # Download the model You can download the model from Hugging Face CLI: ```shell # QiMing-Sales-20B-MXFP4 huggingface-cli download aifeifei798/QiMing-Sales-20B-MXFP4 --local-dir QiMing-Sales-20B-MXFP4/ pip install gpt-oss python -m gpt_oss.chat QiMing-Sales-20B-MXFP4/ ``` # Reasoning levels You can adjust the reasoning level that suits your task across three levels: * **Low:** Fast responses for general dialogue. * **Medium:** Balanced speed and detail. * **High:** Deep and detailed analysis. The reasoning level can be set in the system prompts, e.g., "Reasoning: high". # Tool use The gpt-oss models are excellent for: * Web browsing (using built-in browsing tools) * Function calling with defined schemas * Agentic operations like browser tasks # Fine-tuning QiMing-Sales-20B-MXFP4 models can be fine-tuned for a variety of specialized use cases. This smaller model `QiMing-Sales-20B-MXFP4` can be fine-tuned on consumer hardware ## Citation If you use QiMing-Sales-20B-MXFP4 in your research or application, please cite the model creator. ```bibtex @software{qiming_sales_2025, author = {aifeifei798}, title = {QiMing-Sales-20B-MXFP4: A Cognitive Simulator for B2B Sales Expertise}, month = {September}, year = {2025}, url = {https://huggingface.co/aifeifei798/QiMing-Sales-20B-MXFP4} } ```