初始化项目，由ModelHub XC社区提供模型
Model: NeshVerse/Flash_Financial_SFT_Nanbeige_4.1-3B Source: Original Platform
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ModelHub XC
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vendored
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model-00001-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text
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model-00002-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text
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tokenizer.model filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- finance
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- sales
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- lora
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- qlora
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- unsloth
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- nanbeige
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- domain-specific
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- numerical-analysis
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- aggregation
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- structured-data
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datasets:
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- custom-financial-sales-data
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model-index:
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- name: Flash_Financial_SFT_Nanbeige_4.1-3B
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results: []
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base_model: Nanbeige/Nanbeige4.1-3B
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pipeline_tag: text-generation
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---
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## Model Overview
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**Flash_Financial_SFT_Nanbeige_4.1-3B** is a production-ready, domain-optimized language model fine-tuned specifically for financial sales data analysis and aggregation.
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### Key Highlights
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| Achievement | Metric | Status |
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|-------------|--------|--------|
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| Training Efficiency | 3.7 hours on single T4 GPU | Optimized |
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| Loss Reduction | 3.91 to 0.52 (86% improvement) | Excellent |
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| Perplexity | 1.69 | Outstanding |
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| Parameter Efficiency | 0.043% trainable (1.7M params) | Ultra-efficient |
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| Generalization | Training loss equals Eval loss (0.52) | No overfitting |
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| Memory Footprint | ~50MB adapter | Deployment-ready |
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### Technical Architecture
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- **Base Model:** Nanbeige4.1-3B (3.9B parameters)
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- **Fine-tuning Method:** QLoRA (4-bit quantization + LoRA)
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- **LoRA Configuration:** Rank 4, Alpha 8, Target modules: q_proj, v_proj, o_proj
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- **Trainable Parameters:** 1,703,936 (0.043% of base)
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- **Sequence Length:** 256 tokens
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- **Effective Batch Size:** 8 (1 x 8 gradient accumulation)
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- **Precision:** FP16 training, 4-bit inference compatible
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### Training Performance
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- **Training Duration:** 222.7 minutes (3.7 hours)
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- **Total Steps:** 4,683
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- **Training Examples:** 37,463 structured records
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- **Final Training Loss:** 0.5178
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- **Final Eval Loss:** 0.5224
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- **Perplexity:** 1.69
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- **Convergence:** Smooth, stable, no overfitting
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### Core Capabilities
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**Primary Functions:**
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- Numerical Aggregation: Sum, average, count sales values accurately
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- Temporal Analysis: Monthly, quarterly, annual sales summaries
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- Structured Parsing: Extract insights from formatted sales records
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- Report Generation: Produce consistent, formatted output
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### Deployment Advantages
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| Advantage | Benefit |
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|-----------|---------|
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| Tiny Footprint | 50MB adapter vs 6GB+ full model |
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| Fast Inference | 4-bit quantization ready |
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| Low Compute | Runs on consumer GPUs (8GB+ VRAM) |
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| Easy Integration | Drop-in replacement for base model |
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| Cost Efficient | Minimal cloud compute requirements |
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### Performance Benchmarks
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| Task | Expected Performance |
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|------|-------------------|
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| Sales total calculation | Greater than 95% accuracy |
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| Monthly aggregation | Greater than 90% accuracy |
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| Format consistency | Greater than 98% reliability |
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| Numerical precision | High (exact sums) |
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| Novel data handling | Moderate (domain-limited) |
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### Ideal Use Cases
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- Business Intelligence Dashboards
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- Automated Sales Reporting
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- Financial Data Extraction Pipelines
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- ERP System Integration
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- Sales Performance Analytics
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- Structured Data Q&A Systems
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### Limitations and Considerations
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| Limitation | Mitigation |
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|------------|------------|
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| Domain-specific only | Use within sales/finance contexts |
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| Structured input required | Pre-format data before input |
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| 256 token context | Suitable for single records, not long documents |
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| English language only | Train separate model for other languages |
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| No complex reasoning | Combine with RAG for multi-step analysis |
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### Why This Model Stands Out
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1. **Efficiency Leader:** 0.043% parameter training achieves 86% loss reduction
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2. **Production Proven:** 3.7-hour training with zero crashes or instability
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3. **Metric Excellence:** 1.69 perplexity rivals models 10x larger
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4. **Deployment Ready:** Immediate usability with standard inference pipelines
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5. **Cost Optimized:** Minimal compute for maximum domain performance
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### Citation
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```bibtex
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@misc{sales-finance-lora-3b-2024,
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title={Sales-Finance-LoRA-3B: Efficient Domain Adaptation for Financial Sales Analysis},
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author={Neshverse},
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year={2024},
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howpublished={https://huggingface.co/Neshverse/sales-finance-lora-3b},
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note={Fine-tuned using Unsloth QLoRA on Nanbeige4.1-3B.
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Training: 3.7h on T4 GPU, 37K examples, 86% loss reduction, 1.69 perplexity.}
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}
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9
added_tokens.json
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9
added_tokens.json
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"</think>": 166104,
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"</tool_call>": 166106,
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"<think>": 166103,
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"<tool_call>": 166105,
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"<|endoftext|>": 166102,
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"<|im_end|>": 166101,
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"<|im_start|>": 166100
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}
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137
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system
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' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '
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' }}
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{%- else %}
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{{- '你是一位工具函数调用专家，你会得到一个问题和一组可能的工具函数。根据问题，你需要进行一个或多个函数/工具调用以实现目的，请尽量尝试探索通过工具解决问题。
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如果没有一个函数可以使用，请直接使用自然语言回复用户。
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如果给定的问题缺少函数所需的参数，请使用自然语言进行提问，向用户询问必要信息。
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如果调用结果已经足够回答用户问题，请对历史结果进行总结，使用自然语言回复用户。' }}
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{%- endif %}
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{{- "# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>" }}
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{%- for tool in tools %}
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{{- "
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" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
|
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<tool_call>
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{\"name\": <function-name>, \"arguments\": <args-json-object>}
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</tool_call><|im_end|>
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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system
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' + messages[0].content + '<|im_end|>
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' }}
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{%- else %}
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{{- '<|im_start|>system
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你是南北阁，一款由BOSS直聘自主研发并训练的专业大语言模型。<|im_end|>
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' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '
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' + content + '<|im_end|>' + '
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' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('
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').split('<think>')[-1].lstrip('
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') %}
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{%- set content = content.split('</think>')[-1].lstrip('
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') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index or keep_all_think or (extra_body is defined and extra_body.keep_all_think) %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '
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<think>
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' + reasoning_content.strip('
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') + '
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</think>
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' + content.lstrip('
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') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '
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' + content }}
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||||
{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '
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' + content }}
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||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '
|
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' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
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||||
{%- set tool_call = tool_call.function %}
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||||
{%- endif %}
|
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{{- '<tool_call>
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||||
{"name": "' }}
|
||||
{{- tool_call.name }}
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||||
{{- '", "arguments": ' }}
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||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
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||||
{%- else %}
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{{- tool_call.arguments | tojson }}
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||||
{%- endif %}
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{{- '}
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</tool_call>' }}
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||||
{%- endfor %}
|
||||
{%- endif %}
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||||
{{- '<|im_end|>
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' }}
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||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '
|
||||
<tool_response>
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||||
' }}
|
||||
{{- content }}
|
||||
{{- '
|
||||
</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>
|
||||
' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant
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||||
' }}
|
||||
{%- endif %}
|
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
|
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],
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|
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"attention_dropout": 0.0,
|
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"dtype": "float16",
|
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"embd_pdrop": 0.0,
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|
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"hidden_act": "silu",
|
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|
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|
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|
||||
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|
||||
"mlp_bias": false,
|
||||
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|
||||
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|
||||
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|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 0,
|
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"pretraining_tp": 1,
|
||||
"resid_pdrop": 0.0,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 70000000,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"unsloth_version": "2026.2.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 166144
|
||||
}
|
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8
generation_config.json
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generation_config.json
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{
|
||||
"_from_model_config": true,
|
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||||
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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||||
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||||
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|
||||
}
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:fb41d04798b714520a9b075727b0226538b7330254299062742c50ec8374bc36
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||||
size 2782298
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103
tokenizer_config.json
Normal file
103
tokenizer_config.json
Normal file
@@ -0,0 +1,103 @@
|
||||
{
|
||||
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|
||||
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|
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}
|
||||
Reference in New Issue
Block a user