Instructions to use rombodawg/Rombos-LLM-V2.5-Qwen-72b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rombodawg/Rombos-LLM-V2.5-Qwen-72b 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-72b")
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-72b")
model = AutoModelForCausalLM.from_pretrained("rombodawg/Rombos-LLM-V2.5-Qwen-72b", device_map="auto")
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-72b 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-72b"
# 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-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rombodawg/Rombos-LLM-V2.5-Qwen-72b

SGLang

How to use rombodawg/Rombos-LLM-V2.5-Qwen-72b 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-72b" \
    --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-72b",
		"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-72b" \
        --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-72b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rombodawg/Rombos-LLM-V2.5-Qwen-72b with Docker Model Runner:
```
docker model run hf.co/rombodawg/Rombos-LLM-V2.5-Qwen-72b
```

Rombos-LLM-V2.5-Qwen-72b / README.md

rombodawg

Adding Evaluation Results (#1)

bb553dd verified almost 2 years ago

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metadata

license: other
library_name: transformers
base_model:
  - Qwen/Qwen2.5-72B-Instruct
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
model-index:
  - name: Replete-LLM-V2.5-Qwen-72b_Duplicated
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 71.55
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 61.27
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 47.58
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 19.8
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 17.32
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 54.83
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Replete-LLM-V2.5-Qwen-72b_Duplicated
          name: Open LLM Leaderboard

Rombos-LLM-V2.5-Qwen-72b

Rombos-LLM-V2.5-Qwen-72b is a continues finetuned version of Qwen2.5-72B. 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-72b-GGUF

EXL2:

Benchmarks: (Coming soon)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	45.39
IFEval (0-Shot)	71.55
BBH (3-Shot)	61.27
MATH Lvl 5 (4-Shot)	47.58
GPQA (0-shot)	19.80
MuSR (0-shot)	17.32
MMLU-PRO (5-shot)	54.83