Instructions to use sometimesanotion/Qwenvergence-14B-v6-Prose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sometimesanotion/Qwenvergence-14B-v6-Prose with Transformers:

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

pipe = pipeline("text-generation", model="sometimesanotion/Qwenvergence-14B-v6-Prose")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sometimesanotion/Qwenvergence-14B-v6-Prose")
model = AutoModelForCausalLM.from_pretrained("sometimesanotion/Qwenvergence-14B-v6-Prose")
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

vLLM

How to use sometimesanotion/Qwenvergence-14B-v6-Prose with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sometimesanotion/Qwenvergence-14B-v6-Prose"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sometimesanotion/Qwenvergence-14B-v6-Prose",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sometimesanotion/Qwenvergence-14B-v6-Prose

SGLang

How to use sometimesanotion/Qwenvergence-14B-v6-Prose 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 "sometimesanotion/Qwenvergence-14B-v6-Prose" \
    --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": "sometimesanotion/Qwenvergence-14B-v6-Prose",
		"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 "sometimesanotion/Qwenvergence-14B-v6-Prose" \
        --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": "sometimesanotion/Qwenvergence-14B-v6-Prose",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sometimesanotion/Qwenvergence-14B-v6-Prose with Docker Model Runner:
```
docker model run hf.co/sometimesanotion/Qwenvergence-14B-v6-Prose
```

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 Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

sometimesanotion/Qwenvergence-14B-v6-Prose-slerp

Configuration

The following YAML configuration was used to produce this model:

name:                Qwenvergence-14B-v6-Prose-model_stock
merge_method:        model_stock
base_model:          Qwen/Qwen2.5-14B
tokenizer_source:    huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
parameters:
  int8_mask:         true
  normalize:         true
  rescale:           false
models:
  - model:           arcee-ai/Virtuoso-Small
  - model:           sometimesanotion/Lamarck-14B-v0.3
  - model:           EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
  - model:           allura-org/TQ2.5-14B-Sugarquill-v1
  - model:           oxyapi/oxy-1-small
  - model:           v000000/Qwen2.5-Lumen-14B
  - model:           sthenno-com/miscii-14b-1225
  - model:           sthenno-com/miscii-14b-1225
  - model:           underwoods/medius-erebus-magnum-14b
  - model:           huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
dtype:               float32
out_dtype:           bfloat16
---
# Nifty TIES, LoRA, SLERP involving the listed models
---
name:                Qwenvergence-14B-v6-Prose
merge_method:        ties
base_model:          Qwen/Qwen2.5-14B
tokenizer_source:    base
parameters:         
  density:           1.00
  weight:            1.00
  int8_mask:         true
  normalize:         true
  rescale:           false
dtype:               float32
out_dtype:           bfloat16
models:
  - model:           sometimesanotion/Qwenvergence-14B-v6-Prose-slerp
    parameters:
      density:       1.00
      weight:        1.00

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Safetensors

Model size

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Tensor type

BF16

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Paper for sometimesanotion/Qwenvergence-14B-v6-Prose

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19