Instructions to use Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8 with Transformers:

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

pipe = pipeline("text-generation", model="Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8")
model = AutoModelForMultimodalLM.from_pretrained("Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8")
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 Settings

vLLM

How to use Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8

SGLang

How to use Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8 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 "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8" \
    --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": "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8",
		"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 "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8" \
        --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": "Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8 with Docker Model Runner:
```
docker model run hf.co/Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

This is a merge of Bytedance Seed-OSS-36B Base and Instruct, using the karcher-means method in mergekit, with the idea being to get Bytedance Instruct to 'feel' and write more like a raw continuation model.

Karcher was tested because this and SLERP are seemingly the only viable ways to merge an instruct and base model.

Quantized, it gets an MMLU score (via the exllamav3 eval script) of 11853/ 14042 = 84.41% correct, ( 80.41% prob.)

For reference, ByteDance's instruct model (with the exact same quantization settings) gets 11680/ 14042 = 83.18% correct, ( 80.96% prob.) The base model by itself: 11851/ 14042 = 84.40% correct, ( 76.96% prob.)

This upload is a custom ~4.22bpw exl3 quantization, with 5bpw attention heads and 4bpw MLP layers. If you want a different size quantization, just ask.

Merge Details

Merge Method

This model was merged using the Karcher Mean merge method using /home/alpha/Models/Raw/ByteDance-Seed_Seed-OSS-36B-Instruct as a base.

Models Merged

The following models were included in the merge:

/home/alpha/Models/Raw/ByteDance-Seed_Seed-OSS-36B-Base

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /home/alpha/Models/Raw/ByteDance-Seed_Seed-OSS-36B-Base
  - model: /home/alpha/Models/Raw/ByteDance-Seed_Seed-OSS-36B-Instruct
merge_method: karcher
tokenizer:
  source: "base"
base_model: /home/alpha/Models/Raw/ByteDance-Seed_Seed-OSS-36B-Instruct
parameters:
  int8_mask: true
dtype: bfloat16

Downloads last month: 4

Safetensors

Model size

10B params

Tensor type

F16

I16

Model tree for Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge-exl3-4.22bpw-hb8

Base model

Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge

Quantized

(4)

this model