Instructions to use IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2

SGLang

How to use IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 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 "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2" \
    --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": "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2",
		"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 "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2" \
        --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": "IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/IHaBiS/Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2
```

Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2

File size: 1,966 Bytes

exl2 version of [Undi95/Mistral-11B-TestBench3](https://huggingface.co/Undi95/Mistral-11B-TestBench3)  
dataset : [wikitext](https://huggingface.co/datasets/wikitext)  
command : python convert.py -i models/Undi95_Mistral-11B-TestBench3 -o Undi95_Mistral-11B-TestBench3-temp -cf Undi95_Mistral-11B-TestBench3-4.0bpw-h8-exl2 -c 0000.parquet -l 4096 -b 4 -hb 8 -ss 4096  

Under this sentence is original model card.

```
slices:
  - sources:
    - model: Norquinal/Mistral-7B-claude-chat
      layer_range: [0, 24]
  - sources:
    - model: Open-Orca/Mistral-7B-OpenOrca
      layer_range: [8, 32]
merge_method: passthrough
dtype: float16

========================================================

slices:
  - sources:
      - model: Undi95/Mistral-11B-CC-Air
        layer_range: [0, 48]
      - model: "/content/drive/MyDrive/Mistral-11B-ClaudeOrca"
        layer_range: [0, 48]
merge_method: slerp
base_model: Undi95/Mistral-11B-CC-Air
parameters:
  t:
    - value: 0.5 # fallback for rest of tensors
dtype: float16
```

hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4
|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.5401|±  |0.0146|
|             |       |acc_norm|0.5589|±  |0.0145|
|arc_easy     |      0|acc     |0.8199|±  |0.0079|
|             |       |acc_norm|0.8127|±  |0.0080|
|hellaswag    |      0|acc     |0.6361|±  |0.0048|
|             |       |acc_norm|0.8202|±  |0.0038|
|piqa         |      0|acc     |0.8079|±  |0.0092|
|             |       |acc_norm|0.8199|±  |0.0090|
|truthfulqa_mc|      1|mc1     |0.3733|±  |0.0169|
|             |       |mc2     |0.5374|±  |0.0156|
|winogrande   |      0|acc     |0.7261|±  |0.0125|


![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/Zhnr4xMyetlE950EYkEws.png)