Instructions to use NeverSleep/Mistral-11B-OmniMix-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeverSleep/Mistral-11B-OmniMix-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="NeverSleep/Mistral-11B-OmniMix-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NeverSleep/Mistral-11B-OmniMix-bf16")
model = AutoModelForCausalLM.from_pretrained("NeverSleep/Mistral-11B-OmniMix-bf16")
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 NeverSleep/Mistral-11B-OmniMix-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeverSleep/Mistral-11B-OmniMix-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NeverSleep/Mistral-11B-OmniMix-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeverSleep/Mistral-11B-OmniMix-bf16

SGLang

How to use NeverSleep/Mistral-11B-OmniMix-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 "NeverSleep/Mistral-11B-OmniMix-bf16" \
    --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": "NeverSleep/Mistral-11B-OmniMix-bf16",
		"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 "NeverSleep/Mistral-11B-OmniMix-bf16" \
        --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": "NeverSleep/Mistral-11B-OmniMix-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NeverSleep/Mistral-11B-OmniMix-bf16 with Docker Model Runner:
```
docker model run hf.co/NeverSleep/Mistral-11B-OmniMix-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model should be fixed, it was MEANT to be BF16.

Don't mind this one at the moment, I need to finetune it for RP, it's just a test.

Description

This repo contains fp16 files of Mistral-11B-OmniMix-bf16.

My goal for this model was only to make it score the highest possible with merge and layer toying, proving that:

Benchmark are objective
You should try a model yourself and don't go blindly to the highest rated one
Merge/Layer toying CAN be usable to do better model (maybe?)

Model used

Prompt template

The best one after further testing is this one:

<|system|>
Below is an instruction that describes a task. Write a response that appropriately completes the request.
<|user|>
{prompt}
<|assistant|>

But these one work too:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

USER: <prompt>
ASSISTANT:

Or use any prompting system from one of the 4 source model, should work.

The secret sauce

Mistral-11B-OpenOrcaPlatypus :

slices:
  - sources:
    - model: Open-Orca/Mistral-7B-OpenOrca
      layer_range: [0, 24]
  - sources:
    - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Mistral-11B-CC-Zephyr :

slices:
  - sources:
    - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
      layer_range: [0, 24]
  - sources:
    - model: "/content/drive/MyDrive/Zephyr-7B"
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Mistral-11B-OmniMix :

slices:
  - sources:
      - model: Mistral-11B-OpenOrcaPlatypus
        layer_range: [0, 48]
      - model: Mistral-11B-CC-Zephyr
        layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
  t:
    - filter: lm_head 
      value: [0.75]
    - filter: embed_tokens
      value: [0.75]
    - filter: self_attn
      value: [0.75, 0.25]
    - filter: mlp
      value:  [0.25, 0.75]
    - filter: layernorm
      value: [0.5, 0.5]
    - filter: modelnorm
      value: [0.75]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

I use mergekit for all the manipulation told here.

Some scoring I done myself

hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-OmniMix-bf16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	0.5580	±	0.0145
		acc_norm	0.5819	±	0.0144
arc_easy	0	acc	0.8300	±	0.0077
		acc_norm	0.8211	±	0.0079
hellaswag	0	acc	0.6372	±	0.0048
		acc_norm	0.8209	±	0.0038
piqa	0	acc	0.8145	±	0.0091
		acc_norm	0.8286	±	0.0088
truthfulqa_mc	1	mc1	0.3978	±	0.0171
		mc2	0.5680	±	0.0155
winogrande	0	acc	0.7427	±	0.0123

Others

Special thanks to Sushi, Henky for the machine he give me for big task, and Charles Goddard for his amazing tool.

If you want to support me, you can here.

Downloads last month: 213

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for NeverSleep/Mistral-11B-OmniMix-bf16

Quantizations

3 models