Text Generation
Transformers
Safetensors
mistral
4-bit precision
AWQ
conversational
text-generation-inference
awq
Instructions to use solidrust/Mixtral_AI_MiniTron_Chat-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") 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 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
| base_model: LeroyDyer/Mixtral_AI_MiniTron_Chat | |
| library_name: transformers | |
| tags: | |
| - 4-bit | |
| - AWQ | |
| - text-generation | |
| - autotrain_compatible | |
| - endpoints_compatible | |
| pipeline_tag: text-generation | |
| inference: false | |
| quantized_by: Suparious | |
| # LeroyDyer/Mixtral_AI_MiniTron_Chat AWQ | |
| - Model creator: [LeroyDyer](https://huggingface.co/LeroyDyer) | |
| - Original model: [Mixtral_AI_MiniTron_Chat](https://huggingface.co/LeroyDyer/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 | |