Instructions to use rombodawg/Rombos-LLM-V2.5-Qwen-32b 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 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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rombodawg/Rombos-LLM-V2.5-Qwen-32b")
model = AutoModelForMultimodalLM.from_pretrained("rombodawg/Rombos-LLM-V2.5-Qwen-32b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use rombodawg/Rombos-LLM-V2.5-Qwen-32b 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Fine tuning process for "<|im_start|>" and "<|im_end|>" tokens

by ccdv - opened Oct 12, 2024

Discussion

ccdv

Oct 12, 2024

Hi
Your continuous fine tuning techniques requires to fine tune the base model using lora.
Do you also fine tune the embedding layer and the lm_head using lora?
Because its difficult to match the instruct template without training "<|im_start|>" and "<|im_end|>" tokens.

thank you

ccdv changed discussion title from Fine tuning process to Fine tuning process for "<|im_start|>" and "<|im_end|>" tokens Oct 12, 2024

rombodawg

Owner Oct 12, 2024

It just depends on the prompt template you are using. Generally you want to match the tokens of the target models prompt template for ease of use. So id recommend finetuning in the same tokens yes.

ccdv

Oct 13, 2024

Thank you.
Can you share the lora config that you used with unsloth please?
I'm trying to fine tune a small qwen base model on a niche dataset but I dont find the way to tune chat template tokens.

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