Instructions to use Kquant03/NurseButtercup-4x7B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/NurseButtercup-4x7B-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="Kquant03/NurseButtercup-4x7B-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kquant03/NurseButtercup-4x7B-bf16")
model = AutoModelForCausalLM.from_pretrained("Kquant03/NurseButtercup-4x7B-bf16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kquant03/NurseButtercup-4x7B-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/NurseButtercup-4x7B-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/NurseButtercup-4x7B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kquant03/NurseButtercup-4x7B-bf16

SGLang

How to use Kquant03/NurseButtercup-4x7B-bf16 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 "Kquant03/NurseButtercup-4x7B-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/NurseButtercup-4x7B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Kquant03/NurseButtercup-4x7B-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/NurseButtercup-4x7B-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kquant03/NurseButtercup-4x7B-bf16 with Docker Model Runner:
```
docker model run hf.co/Kquant03/NurseButtercup-4x7B-bf16
```

Unable to create exl2 quant for this model

by Samvanity - opened Apr 16, 2024

Discussion

Samvanity

Apr 16, 2024

Hi,

I'm trying to create an exl2 quant for this model but I ran into this error:

| Measured: model.layers.0 (Attention) |
| Duration: 8.47 seconds |
| Completed step: 1/67 |
| Avg time / step (rolling): 8.47 seconds |
| Estimated remaining time: 9min 18sec |
| Last checkpoint layer: None |

-- Layer: model.layers.0 (MoE MLP)
!! Warning: w2.2 has less than 10% calibration for 19/19 rows
!! Warning: w2.3 has less than 10% calibration for 19/19 rows
Traceback (most recent call last):
File "E:\ai\Exl2\exllamav2\convert.py", line 219, in
status = measure_quant(job, save_job, model) # capturing the graceful exits
File "E:\ai\pinokio\bin\miniconda\envs\exl2\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "E:\ai\Exl2\exllamav2\conversion\measure.py", line 538, in measure_quant
m = measure_moe_mlp(module, hidden_states, target_states, quantizers, cache, attn_params)
File "E:\ai\Exl2\exllamav2\conversion\measure.py", line 273, in measure_moe_mlp
quantizers[f"w2.{i}"].prepare()
File "E:\ai\Exl2\exllamav2\conversion\adaptivegptq.py", line 225, in prepare
self.hessian /= self.num_batches
TypeError: unsupported operand type(s) for /=: 'NoneType' and 'int'

I was able to quantize Buttercup-4x7B-V2-laser and others, but not this one. I'm not sure what I have to do to quantize it. I'm using the latest exllamav2 v0.0.18

Thanks!

Kquant03

Owner Apr 16, 2024

I was able to quantize Buttercup-4x7B-V2-laser and others, but not this one. I'm not sure what I have to do to quantize it. I'm using the latest exllamav2 v0.0.18

Thanks!

So, I asked the creator of exllama and he says it's because some of my experts aren't activating at all during quantization...might be better to use a different dataset in the quantization

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment

Unable to create exl2 quant for this model

| Measured: model.layers.0 (Attention) || Duration: 8.47 seconds || Completed step: 1/67 || Avg time / step (rolling): 8.47 seconds || Estimated remaining time: 9min 18sec || Last checkpoint layer: None |

| Measured: model.layers.0 (Attention) |
| Duration: 8.47 seconds |
| Completed step: 1/67 |
| Avg time / step (rolling): 8.47 seconds |
| Estimated remaining time: 9min 18sec |
| Last checkpoint layer: None |