Instructions to use TheBloke/Mixtral-8x7B-v0.1-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Mixtral-8x7B-v0.1-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/Mixtral-8x7B-v0.1-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mixtral-8x7B-v0.1-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/Mixtral-8x7B-v0.1-GPTQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/Mixtral-8x7B-v0.1-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/Mixtral-8x7B-v0.1-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/Mixtral-8x7B-v0.1-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ
- SGLang
How to use TheBloke/Mixtral-8x7B-v0.1-GPTQ 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 "TheBloke/Mixtral-8x7B-v0.1-GPTQ" \ --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": "TheBloke/Mixtral-8x7B-v0.1-GPTQ", "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 "TheBloke/Mixtral-8x7B-v0.1-GPTQ" \ --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": "TheBloke/Mixtral-8x7B-v0.1-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/Mixtral-8x7B-v0.1-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ
File size: 2,205 Bytes
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"_name_or_path": "/workspace/process/mistralai_mixtral-8x7b-v0.1/source",
"architectures": [
"MixtralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mixtral",
"num_attention_heads": 32,
"num_experts_per_tok": 2,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"num_local_experts": 8,
"output_router_logits": false,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000.0,
"router_aux_loss_coef": 0.02,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.36.0",
"use_cache": true,
"vocab_size": 32000,
"quantization_config": {
"bits": 4,
"modules_in_block_to_quantize" : [
["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"],
["self_attn.o_proj"],
["block_sparse_moe.experts.0.w1", "block_sparse_moe.experts.0.w2", "block_sparse_moe.experts.0.w3"],
["block_sparse_moe.experts.1.w1", "block_sparse_moe.experts.1.w2", "block_sparse_moe.experts.1.w3"],
["block_sparse_moe.experts.2.w1", "block_sparse_moe.experts.2.w2", "block_sparse_moe.experts.2.w3"],
["block_sparse_moe.experts.3.w1", "block_sparse_moe.experts.3.w2", "block_sparse_moe.experts.3.w3"],
["block_sparse_moe.experts.4.w1", "block_sparse_moe.experts.4.w2", "block_sparse_moe.experts.4.w3"],
["block_sparse_moe.experts.5.w1", "block_sparse_moe.experts.5.w2", "block_sparse_moe.experts.5.w3"],
["block_sparse_moe.experts.6.w1", "block_sparse_moe.experts.6.w2", "block_sparse_moe.experts.6.w3"],
["block_sparse_moe.experts.7.w1", "block_sparse_moe.experts.7.w2", "block_sparse_moe.experts.7.w3"]],
"group_size": -1,
"damp_percent": 0.1,
"desc_act": true,
"sym": true,
"true_sequential": true,
"model_name_or_path": null,
"model_file_base_name": "model",
"quant_method": "gptq"
}
}
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