Instructions to use Envoid/Mixtral-Instruct-ITR-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Envoid/Mixtral-Instruct-ITR-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="Envoid/Mixtral-Instruct-ITR-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Envoid/Mixtral-Instruct-ITR-8x7B")
model = AutoModelForCausalLM.from_pretrained("Envoid/Mixtral-Instruct-ITR-8x7B")
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

vLLM

How to use Envoid/Mixtral-Instruct-ITR-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Envoid/Mixtral-Instruct-ITR-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/Mixtral-Instruct-ITR-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Envoid/Mixtral-Instruct-ITR-8x7B

SGLang

How to use Envoid/Mixtral-Instruct-ITR-8x7B 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 "Envoid/Mixtral-Instruct-ITR-8x7B" \
    --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": "Envoid/Mixtral-Instruct-ITR-8x7B",
		"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 "Envoid/Mixtral-Instruct-ITR-8x7B" \
        --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": "Envoid/Mixtral-Instruct-ITR-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Envoid/Mixtral-Instruct-ITR-8x7B with Docker Model Runner:
```
docker model run hf.co/Envoid/Mixtral-Instruct-ITR-8x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Caution this model may be unpredictable

Mixtral-Instruct-ITR (Interpolative Training Regression)

We have to go back, edition.

For this model I took what I learned in the making of Cat-8x7B and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto mistralai/Mixtral-8x7B-v0.1

While the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being.

The hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun.

Apologies about the small shard size (keep forgetting to change the mergekit config back)

The model is a lot less likely to refuse certain requests in this state:

so if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case.

The model still responds well to [INST] Thingie [/INST] formatting quite well.

Or if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config

models:
  - model: ./mistralai_Mixtral-8x7B-Instruct-v0.1
  - model: ./mistralai_Mixtral-8x7B-v0.1
merge_method: slerp
base_model: ./mistralai_Mixtral-8x7B-v0.1
parameters:
  t:
    - value: 0.5
dtype: float16

Downloads last month: 8

Safetensors

Model size

47B params

Tensor type

F16

Model tree for Envoid/Mixtral-Instruct-ITR-8x7B

Merges

1 model

Quantizations

2 models