Instructions to use RobertML/sn6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobertML/sn6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RobertML/sn6b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RobertML/sn6b") model = AutoModelForCausalLM.from_pretrained("RobertML/sn6b") 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
- vLLM
How to use RobertML/sn6b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RobertML/sn6b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RobertML/sn6b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RobertML/sn6b
- SGLang
How to use RobertML/sn6b 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 "RobertML/sn6b" \ --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": "RobertML/sn6b", "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 "RobertML/sn6b" \ --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": "RobertML/sn6b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RobertML/sn6b with Docker Model Runner:
docker model run hf.co/RobertML/sn6b
Upload StableLmForCausalLM
Browse files- config.json +29 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
config.json
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{
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"_name_or_path": "neurons/model-best-0.9991267374247486",
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"architectures": [
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"StableLmForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 100289,
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"eos_token_id": 100290,
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"hidden_act": "silu",
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"hidden_dropout": 0.0,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 4096,
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"model_type": "stablelm",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"partial_rotary_factor": 0.25,
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"rope_scaling": null,
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"rope_theta": 10000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.38.2",
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"use_cache": false,
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"use_qkv_bias": true,
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"vocab_size": 100352
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 100289,
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"eos_token_id": 100290,
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"transformers_version": "4.38.2",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:834127ac6df37e5d7443df3884a9217fd3804798d9f7cdfe9a70e9adc16dab6b
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size 3289069520
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