File size: 1,307 Bytes
42a3a14 f57620f a9baed2 42a3a14 a9baed2 42a3a14 a9baed2 42a3a14 a9baed2 42a3a14 a9baed2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
---
base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Qwen2.5-Coder-32B-Instruct-WMX
Pre-fine-tuned LoRA adapters for unsloth/Qwen2.5-Coder-32B-Instruct.
**This lora adapters have been fine-tuned for WMX services using the folowing datasets.**
- https://huggingface.co/datasets/Jake5/movensys-info
- https://huggingface.co/datasets/Jake5/wmx-doc-user
- https://huggingface.co/datasets/Jake5/wmx-doc-robot
## Version v0.9
- Source: lora_model
- Base model: unsloth/Qwen2.5-Coder-32B-Instruct
- Uploaded on: 2025-09-12
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.9")
tokenizer = AutoTokenizer.from_pretrained("Jake5/Qwen2.5-Coder-32B-Instruct-WMX", subfolder="adapters_v0.9")
```
## vLLM Serving
```bash
python -m vllm.entrypoints.openai.api_server \
--model unsloth/Qwen2.5-Coder-32B-Instruct \
--lora-modules my-lora=Jake5/Qwen2.5-Coder-32B-Instruct-WMX/adapters_v0.9 \
--dtype bfloat16 \
--port 8000
```
|