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
- Unsloth Studio new
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, )
analysis-llm-v1
这是一个基于 DeepSeek-R1-Distill-Llama-8B 微调的前端需求分析模型。
使用方法
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "MANSTAGE/analysis-llm-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
# 推理代码...
训练详情
- 基础模型: unsloth/DeepSeek-R1-Distill-Llama-8B
- 训练数据: 219条前端需求分析数据
- 训练步数: 100步
- LoRA配置: r=16, alpha=16
- Downloads last month
- -
Model tree for MANSTAGE/analysis-llm-v1
Base model
deepseek-ai/DeepSeek-R1-Distill-Llama-8B Finetuned
unsloth/DeepSeek-R1-Distill-Llama-8B
Task type is invalid.