Text Generation
PEFT
Safetensors
llama
frontend
analysis
requirements
chinese
lora
sft
trl
unsloth
conversational
Instructions to use MANSTAGE/analysis-llm-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MANSTAGE/analysis-llm-v1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use MANSTAGE/analysis-llm-v1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MANSTAGE/analysis-llm-v1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for MANSTAGE/analysis-llm-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MANSTAGE/analysis-llm-v1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="MANSTAGE/analysis-llm-v1", max_seq_length=2048, )
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
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tags:
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- text-generation
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- frontend
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- analysis
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- requirements
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- chinese
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- lora
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- peft
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- sft
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- trl
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- unsloth
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- conversational
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pipeline_tag: text-generation
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---
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# analysis-llm-v1
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这是一个基于 DeepSeek-R1-Distill-Llama-8B 微调的前端需求分析模型。
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## 使用方法
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "MANSTAGE/analysis-llm-v1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
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# 推理代码...
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```
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## 训练详情
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- **基础模型**: unsloth/DeepSeek-R1-Distill-Llama-8B
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- **训练数据**: 219条前端需求分析数据
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- **训练步数**: 100步
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- **LoRA配置**: r=16, alpha=16
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