Instructions to use Jake5/Qwen2.5-Coder-32B-Instruct-WMX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jake5/Qwen2.5-Coder-32B-Instruct-WMX with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jake5/Qwen2.5-Coder-32B-Instruct-WMX", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Jake5/Qwen2.5-Coder-32B-Instruct-WMX 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 Jake5/Qwen2.5-Coder-32B-Instruct-WMX 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 Jake5/Qwen2.5-Coder-32B-Instruct-WMX to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Jake5/Qwen2.5-Coder-32B-Instruct-WMX to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Jake5/Qwen2.5-Coder-32B-Instruct-WMX", max_seq_length=2048, )
Update model card for v0.7
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README.md
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---
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base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit
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tags:
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- https://huggingface.co/datasets/Jake5/wmx-doc-user
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- https://huggingface.co/datasets/Jake5/wmx-doc-robot
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## Version v0.
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- Source: lora_model
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- Base model: unsloth/Qwen2.5-Coder-32B-Instruct
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- Uploaded on: 2025-09-
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## Usage
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```python
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from peft import PeftModel
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-32B-Instruct")
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model = PeftModel.from_pretrained(base_model, "Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.
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tokenizer = AutoTokenizer.from_pretrained("Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.
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```
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## vLLM Serving
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model unsloth/Qwen2.5-Coder-32B-Instruct \
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--lora-modules my-lora=Jake5/Qwen2.5-Coder-32B-Instruct-WMX/adapters_v0.
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--dtype bfloat16 \
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--port 8000
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```
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---
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base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit
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tags:
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- https://huggingface.co/datasets/Jake5/wmx-doc-user
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- https://huggingface.co/datasets/Jake5/wmx-doc-robot
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## Version v0.7
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- Source: lora_model
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- Base model: unsloth/Qwen2.5-Coder-32B-Instruct
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- Uploaded on: 2025-09-10
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## Usage
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```python
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from peft import PeftModel
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-32B-Instruct")
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model = PeftModel.from_pretrained(base_model, "Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.7")
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tokenizer = AutoTokenizer.from_pretrained("Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.7")
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```
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## vLLM Serving
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model unsloth/Qwen2.5-Coder-32B-Instruct \
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--lora-modules my-lora=Jake5/Qwen2.5-Coder-32B-Instruct-WMX/adapters_v0.7 \
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--dtype bfloat16 \
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--port 8000
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```
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