Instructions to use rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit")
model = AutoModelForCausalLM.from_pretrained("rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit

SGLang

How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit with Docker Model Runner:
```
docker model run hf.co/rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit
```

Rombos-LLM-V2.5-Qwen-32b

Here are the best params to use for this model, and possibly other qwen models in the oobagooba text generation web ui. From my testing at least:

Rombos-LLM-V2.5-Qwen-32b is a continues finetuned version of Qwen2.5-32B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method

This version of the model shows higher performance than the original instruct and base models.

Quants: (Coming soon)

GGUF: https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-32b-GGUF

EXL2:

Benchmarks: (Coming soon)

Downloads last month: 2

Model tree for rombodawg/Rombos-LLM-V2.5-Qwen-32b-Exl2-4.25-bit

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

(1218)

this model