Instructions to use solidrust/Mixtral_AI_MiniTron_Chat-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Mixtral_AI_MiniTron_Chat-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/Mixtral_AI_MiniTron_Chat-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Mixtral_AI_MiniTron_Chat-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Mixtral_AI_MiniTron_Chat-AWQ")
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 solidrust/Mixtral_AI_MiniTron_Chat-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/solidrust/Mixtral_AI_MiniTron_Chat-AWQ

SGLang

How to use solidrust/Mixtral_AI_MiniTron_Chat-AWQ 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 "solidrust/Mixtral_AI_MiniTron_Chat-AWQ" \
    --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": "solidrust/Mixtral_AI_MiniTron_Chat-AWQ",
		"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 "solidrust/Mixtral_AI_MiniTron_Chat-AWQ" \
        --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": "solidrust/Mixtral_AI_MiniTron_Chat-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/Mixtral_AI_MiniTron_Chat-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Mixtral_AI_MiniTron_Chat-AWQ
```

LeroyDyer/Mixtral_AI_MiniTron_Chat AWQ

Model creator: LeroyDyer
Original model: Mixtral_AI_MiniTron_Chat

Model Summary

these little one are easy to train for task !!! ::

They already have some training (not great) But they can take more and more

(and being MISTRAL they can takes lora modules!)

Rememeber to add training on to the lora you merge withit : ie load the lora and train a few cycle on the same data that was applied in the p=lora (ie 20 Steps ) and

See it it took hold then merge IT!

Developed by: LeroyDyer
License: apache-2.0
Finetuned from model : LeroyDyer/Mixtral_AI_MiniTron

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

I32

F16