Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
Merge
mergekit
Mistral
openchat/openchat-3.5-1210
beowolx/CodeNinja-1.0-OpenChat-7B
maywell/PiVoT-0.1-Starling-LM-RP
WizardLM/WizardMath-7B-V1.1
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use mlabonne/Beyonder-4x7B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlabonne/Beyonder-4x7B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlabonne/Beyonder-4x7B-v2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlabonne/Beyonder-4x7B-v2") model = AutoModelForCausalLM.from_pretrained("mlabonne/Beyonder-4x7B-v2", 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 mlabonne/Beyonder-4x7B-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlabonne/Beyonder-4x7B-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/Beyonder-4x7B-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mlabonne/Beyonder-4x7B-v2
- SGLang
How to use mlabonne/Beyonder-4x7B-v2 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 "mlabonne/Beyonder-4x7B-v2" \ --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": "mlabonne/Beyonder-4x7B-v2", "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 "mlabonne/Beyonder-4x7B-v2" \ --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": "mlabonne/Beyonder-4x7B-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mlabonne/Beyonder-4x7B-v2 with Docker Model Runner:
docker model run hf.co/mlabonne/Beyonder-4x7B-v2
Upload folder using huggingface_hub
Browse files- config.json +2 -2
- mergekit_moe_config.yml +31 -0
- model-00005-of-00005.safetensors +1 -1
- model.safetensors.index.json +0 -0
- tokenizer_config.json +2 -3
config.json
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings":
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"model_type": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 2,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 32000
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}
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 2,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.1",
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"use_cache": true,
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"vocab_size": 32000
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}
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mergekit_moe_config.yml
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base_model: mlabonne/Marcoro14-7B-slerp
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experts:
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- source_model: openchat/openchat-3.5-1210
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positive_prompts:
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- "chat"
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- "assistant"
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- "tell me"
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- "explain"
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- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
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positive_prompts:
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- "code"
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- "python"
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- "javascript"
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- "programming"
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- "algorithm"
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- source_model: maywell/PiVoT-0.1-Starling-LM-RP
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positive_prompts:
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- "storywriting"
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- "write"
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- "scene"
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- "story"
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- "character"
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- source_model: WizardLM/WizardMath-7B-V1.1
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positive_prompts:
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- "reason"
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- "math"
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- "mathematics"
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- "solve"
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- "count"
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tokenizer_source: union
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model-00005-of-00005.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 8440279464
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1e3cc831230c664a8590c85633fccc26a93e51a0bcfb8ea3dd9039c22847632
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size 8440279464
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model.safetensors.index.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length":
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"pad_token": "<s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"chat_template": "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + eos_token}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
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"use_default_system_prompt": true
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}
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": true
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}
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