Instructions to use Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview")
model = AutoModelForCausalLM.from_pretrained("Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview")
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 Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview

SGLang

How to use Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview 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 "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview" \
    --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": "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview",
		"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 "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview" \
        --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": "Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview with Docker Model Runner:
```
docker model run hf.co/Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview
```

Qwen2.5-Dyanka-7B-Preview / README.md

Xiaojian9992024

Update README.md

95886fb verified over 1 year ago

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metadata

base_model:
  - rombodawg/Rombos-LLM-V2.5-Qwen-7b
  - suayptalha/Clarus-7B-v0.1
  - gz987/qwen2.5-7b-cabs-v0.3
  - prithivMLmods/WebMind-7B-v0.1
  - fblgit/cybertron-v4-qw7B-MGS
  - Xiaojian9992024/Qwen2.5-THREADRIPPER-Small
library_name: transformers
tags:
  - mergekit
  - merge
model-index:
  - name: Qwen2.5-Dyanka-7B-Preview
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: wis-k/instruction-following-eval
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 76.4
            name: averaged accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: SaylorTwift/bbh
          split: test
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 36.62
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: lighteval/MATH-Hard
          split: test
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 48.79
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 8.95
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          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: 15.51
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          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: 37.51
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Xiaojian9992024%2FQwen2.5-Dyanka-7B-Preview
          name: Open LLM Leaderboard
license: apache-2.0

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using gz987/qwen2.5-7b-cabs-v0.3 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: gz987/qwen2.5-7b-cabs-v0.3
    #no parameters necessary for base model
  - model: suayptalha/Clarus-7B-v0.1
    parameters:
      density: 0.2
      weight: 0.2
  - model: Xiaojian9992024/Qwen2.5-THREADRIPPER-Small
    parameters:
      density: 0.2
      weight: 0.2
  - model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
    parameters:
      density: 0.2
      weight: 0.2
  - model: prithivMLmods/WebMind-7B-v0.1
    parameters:
      density: 0.2
      weight: 0.2
  - model: fblgit/cybertron-v4-qw7B-MGS
    parameters:
      density: 0.2
      weight: 0.2
      
merge_method: ties
base_model: gz987/qwen2.5-7b-cabs-v0.3
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	37.30
IFEval (0-Shot)	76.40
BBH (3-Shot)	36.62
MATH Lvl 5 (4-Shot)	48.79
GPQA (0-shot)	8.95
MuSR (0-shot)	15.51
MMLU-PRO (5-shot)	37.51