Text Generation
MLX
Safetensors
English
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
dvilasuero/DistilabelBeagle14-7B
beowolx/CodeNinja-1.0-OpenChat-7B
WizardLM/WizardMath-7B-V1.1
Maths
Code
Python
conversational
Instructions to use mlx-community/Pearl-3x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Pearl-3x7B with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Pearl-3x7B") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/Pearl-3x7B with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Pearl-3x7B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Pearl-3x7B" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Pearl-3x7B", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "vocab_size": 32002, | |
| "max_position_embeddings": 32768, | |
| "hidden_size": 4096, | |
| "intermediate_size": 14336, | |
| "num_hidden_layers": 32, | |
| "num_attention_heads": 32, | |
| "sliding_window": null, | |
| "num_key_value_heads": 8, | |
| "hidden_act": "silu", | |
| "initializer_range": 0.02, | |
| "rms_norm_eps": 1e-05, | |
| "use_cache": false, | |
| "rope_theta": 10000.0, | |
| "attention_dropout": 0.0, | |
| "num_experts_per_tok": 2, | |
| "num_local_experts": 3, | |
| "output_router_logits": false, | |
| "router_aux_loss_coef": 0.001, | |
| "router_jitter_noise": 0.0, | |
| "return_dict": true, | |
| "output_hidden_states": false, | |
| "output_attentions": false, | |
| "torchscript": false, | |
| "torch_dtype": "float16", | |
| "use_bfloat16": false, | |
| "tf_legacy_loss": false, | |
| "pruned_heads": {}, | |
| "tie_word_embeddings": false, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "is_decoder": false, | |
| "cross_attention_hidden_size": null, | |
| "add_cross_attention": false, | |
| "tie_encoder_decoder": false, | |
| "max_length": 20, | |
| "min_length": 0, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "num_beams": 1, | |
| "num_beam_groups": 1, | |
| "diversity_penalty": 0.0, | |
| "temperature": 1.0, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "typical_p": 1.0, | |
| "repetition_penalty": 1.0, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 0, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "bad_words_ids": null, | |
| "num_return_sequences": 1, | |
| "output_scores": false, | |
| "return_dict_in_generate": false, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "remove_invalid_values": false, | |
| "exponential_decay_length_penalty": null, | |
| "suppress_tokens": null, | |
| "begin_suppress_tokens": null, | |
| "architectures": [ | |
| "MixtralForCausalLM" | |
| ], | |
| "finetuning_task": null, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "tokenizer_class": null, | |
| "prefix": null, | |
| "bos_token_id": 1, | |
| "pad_token_id": null, | |
| "eos_token_id": 32000, | |
| "sep_token_id": null, | |
| "decoder_start_token_id": null, | |
| "task_specific_params": null, | |
| "problem_type": null, | |
| "_name_or_path": "/home/jupyter/.cache/huggingface/hub/models--louisbrulenaudet--Pearl-3x7B/snapshots/63499a3e77b66d0709c15208720d48e89b4c1786", | |
| "transformers_version": "4.43.4", | |
| "model_type": "mixtral", | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 4 | |
| } | |
| } |