Text Generation
Transformers
Safetensors
GGUF
multilingual
mixtral
coding
Mixture of Experts
conversational
text-generation-inference
Instructions to use davideuler/NebulaNet-v2-4x7B-moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davideuler/NebulaNet-v2-4x7B-moe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="davideuler/NebulaNet-v2-4x7B-moe") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("davideuler/NebulaNet-v2-4x7B-moe") model = AutoModelForCausalLM.from_pretrained("davideuler/NebulaNet-v2-4x7B-moe") 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]:])) - llama-cpp-python
How to use davideuler/NebulaNet-v2-4x7B-moe with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="davideuler/NebulaNet-v2-4x7B-moe", filename="NebulaNet-v2-4x7B.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use davideuler/NebulaNet-v2-4x7B-moe with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K # Run inference directly in the terminal: llama-cli -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K # Run inference directly in the terminal: llama-cli -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K # Run inference directly in the terminal: ./llama-cli -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf davideuler/NebulaNet-v2-4x7B-moe:Q2_K
Use Docker
docker model run hf.co/davideuler/NebulaNet-v2-4x7B-moe:Q2_K
- LM Studio
- Jan
- vLLM
How to use davideuler/NebulaNet-v2-4x7B-moe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "davideuler/NebulaNet-v2-4x7B-moe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "davideuler/NebulaNet-v2-4x7B-moe", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/davideuler/NebulaNet-v2-4x7B-moe:Q2_K
- SGLang
How to use davideuler/NebulaNet-v2-4x7B-moe 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 "davideuler/NebulaNet-v2-4x7B-moe" \ --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": "davideuler/NebulaNet-v2-4x7B-moe", "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 "davideuler/NebulaNet-v2-4x7B-moe" \ --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": "davideuler/NebulaNet-v2-4x7B-moe", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use davideuler/NebulaNet-v2-4x7B-moe with Ollama:
ollama run hf.co/davideuler/NebulaNet-v2-4x7B-moe:Q2_K
- Unsloth Studio
How to use davideuler/NebulaNet-v2-4x7B-moe with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for davideuler/NebulaNet-v2-4x7B-moe to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for davideuler/NebulaNet-v2-4x7B-moe to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for davideuler/NebulaNet-v2-4x7B-moe to start chatting
- Docker Model Runner
How to use davideuler/NebulaNet-v2-4x7B-moe with Docker Model Runner:
docker model run hf.co/davideuler/NebulaNet-v2-4x7B-moe:Q2_K
- Lemonade
How to use davideuler/NebulaNet-v2-4x7B-moe with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull davideuler/NebulaNet-v2-4x7B-moe:Q2_K
Run and chat with the model
lemonade run user.NebulaNet-v2-4x7B-moe-Q2_K
List all available models
lemonade list
david commited on
Commit ·
971c35d
1
Parent(s): a796067
initial upload
Browse files- .gitattributes +1 -0
- NebulaNet-v2-4x7B.Q2_K.gguf +3 -0
- README.md +37 -0
- config.json +30 -0
- mergekit_moe_config.yml +26 -0
- model.safetensors.index.json +1 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +50 -0
.gitattributes
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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NebulaNet-v2-4x7B.Q2_K.gguf
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README.md
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---
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license: mit
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---
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<<<<<<< HEAD
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## Usage
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NebulaNet-v2: An MOE of 4 7b expert models.
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It is good at coding and multi language translation. It should be fluent at chat and math too.
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## mergekit config
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```
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base_model: ContextualAI/Contextual_KTO_Mistral_PairRM
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experts:
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- source_model: ContextualAI/Contextual_KTO_Mistral_PairRM
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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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- "I want"
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- source_model: Nexusflow/Starling-LM-7B-beta
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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: snorkelai/Snorkel-Mistral-PairRM-DPO
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positive_prompts:
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- ""
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- source_model: mlabonne/NeuralDaredevil-7B
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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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```
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=======
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---
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license: mit
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---
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>>>>>>> a796067 (initial commit)
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config.json
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{
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"_name_or_path": "ContextualAI/Contextual_KTO_Mistral_PairRM",
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"architectures": [
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"MixtralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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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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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_local_experts": 4,
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"output_router_logits": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"router_aux_loss_coef": 0.001,
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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": "4.39.1",
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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: ContextualAI/Contextual_KTO_Mistral_PairRM
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experts:
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- source_model: ContextualAI/Contextual_KTO_Mistral_PairRM
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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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- "I want"
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- source_model: Nexusflow/Starling-LM-7B-beta
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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: snorkelai/Snorkel-Mistral-PairRM-DPO
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positive_prompts:
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- ""
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- source_model: mlabonne/NeuralDaredevil-7B
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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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model.safetensors.index.json
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{"metadata": {"mergekit_version": "0.0.4.1"}, "weight_map": {"model.embed_tokens.weight": "model-00001-of-00025.safetensors", "model.norm.weight": "model-00001-of-00025.safetensors", "lm_head.weight": "model-00001-of-00025.safetensors", "model.layers.0.input_layernorm.weight": "model-00001-of-00025.safetensors", "model.layers.0.self_attn.q_proj.weight": "model-00001-of-00025.safetensors", "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00025.safetensors", "model.layers.0.self_attn.v_proj.weight": "model-00001-of-00025.safetensors", "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00025.safetensors", "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.0.w3.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.1.w3.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.2.w3.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.3.w3.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.0.w1.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.1.w1.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.2.w1.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.3.w1.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.0.w2.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.1.w2.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.2.w2.weight": "model-00001-of-00025.safetensors", "model.layers.0.block_sparse_moe.experts.3.w2.weight": "model-00002-of-00025.safetensors", "model.layers.1.input_layernorm.weight": "model-00002-of-00025.safetensors", "model.layers.1.self_attn.q_proj.weight": "model-00002-of-00025.safetensors", "model.layers.1.self_attn.k_proj.weight": "model-00002-of-00025.safetensors", "model.layers.1.self_attn.v_proj.weight": "model-00002-of-00025.safetensors", "model.layers.1.self_attn.o_proj.weight": "model-00002-of-00025.safetensors", "model.layers.1.post_attention_layernorm.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.0.w3.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.1.w3.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.2.w3.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.3.w3.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.0.w1.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.1.w1.weight": "model-00002-of-00025.safetensors", "model.layers.1.block_sparse_moe.experts.2.w1.weight": "model-00002-of-00025.safetensors", 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"model-00025-of-00025.safetensors", "model.layers.27.block_sparse_moe.gate.weight": "model-00025-of-00025.safetensors", "model.layers.28.block_sparse_moe.gate.weight": "model-00025-of-00025.safetensors", "model.layers.29.block_sparse_moe.gate.weight": "model-00025-of-00025.safetensors", "model.layers.30.block_sparse_moe.gate.weight": "model-00025-of-00025.safetensors", "model.layers.31.block_sparse_moe.gate.weight": "model-00025-of-00025.safetensors"}}
|
special_tokens_map.json
ADDED
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| 1 |
+
{
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| 2 |
+
"bos_token": {
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| 3 |
+
"content": "<s>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "</s>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": "<s>",
|
| 17 |
+
"unk_token": {
|
| 18 |
+
"content": "<unk>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
}
|
| 24 |
+
}
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tokenizer.json
ADDED
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tokenizer.model
ADDED
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
|
| 3 |
+
size 493443
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,50 @@
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| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<unk>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<s>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "</s>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
}
|
| 29 |
+
},
|
| 30 |
+
"additional_special_tokens": [],
|
| 31 |
+
"bos_token": "<s>",
|
| 32 |
+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{% if message['role'] == 'user' %}{{ bos_token + '\n<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{{ '<|assistant|>\n' + message['content'] + eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}",
|
| 33 |
+
"clean_up_tokenization_spaces": false,
|
| 34 |
+
"eos_token": "</s>",
|
| 35 |
+
"legacy": true,
|
| 36 |
+
"max_length": 2048,
|
| 37 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 38 |
+
"pad_to_multiple_of": null,
|
| 39 |
+
"pad_token": "<s>",
|
| 40 |
+
"pad_token_type_id": 0,
|
| 41 |
+
"padding_side": "left",
|
| 42 |
+
"sp_model_kwargs": {},
|
| 43 |
+
"spaces_between_special_tokens": false,
|
| 44 |
+
"stride": 0,
|
| 45 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 46 |
+
"truncation_side": "right",
|
| 47 |
+
"truncation_strategy": "longest_first",
|
| 48 |
+
"unk_token": "<unk>",
|
| 49 |
+
"use_default_system_prompt": true
|
| 50 |
+
}
|