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
Update tokenizer_config.json
Browse files- tokenizer_config.json +2 -1
tokenizer_config.json
CHANGED
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@@ -36,11 +36,12 @@
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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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"use_default_system_prompt": true
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}
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| 36 |
"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": 8192,
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| 40 |
"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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