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Model: second-state/Gemma-2-9B-Chinese-Chat-GGUF Source: Original Platform
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---
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license: gemma
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library_name: transformers
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pipeline_tag: text-generation
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base_model: shenzhi-wang/Gemma-2-9B-Chinese-Chat
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inference: false
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model_creator: shenzhi-wang
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model_name: Gemma-2-9B-Chinese-Chat
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quantized_by: Second State Inc.
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language:
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- en
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- zh
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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<!-- header end -->
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# Gemma-2-9B-Chinese-Chat-GGUF
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## Original Model
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[shenzhi-wang/Gemma-2-9B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Gemma-2-9B-Chinese-Chat)
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## Run with LlamaEdge
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- LlamaEdge version: [v0.12.1](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.12.1) and above
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- Prompt template
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- Prompt type: `gemma-instruct`
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- Prompt string
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```text
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<bos><start_of_turn>user
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{user_message}<end_of_turn>
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<start_of_turn>model
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{model_message}<end_of_turn>model
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```
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- Context size: `8192`
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- Run as LlamaEdge service
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```bash
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wasmedge --dir .:. --nn-preload default:GGML:AUTO:Gemma-2-9B-Chinese-Chat-Q5_K_M.gguf \
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llama-api-server.wasm \
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--prompt-template gemma-instruct \
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--ctx-size 8192 \
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--model-name gemma-2-9b
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```
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- Run as LlamaEdge command app
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```bash
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wasmedge --dir .:. \
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--nn-preload default:GGML:AUTO:Gemma-2-9B-Chinese-Chat-Q5_K_M.gguf \
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llama-chat.wasm \
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--prompt-template gemma-instruct \
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--ctx-size 8192
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```
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## Quantized GGUF Models
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| Name | Quant method | Bits | Size | Use case |
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| ---- | ---- | ---- | ---- | ----- |
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| [Gemma-2-9B-Chinese-Chat-Q2_K.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q2_K.gguf) | Q2_K | 2 | 3.81 GB| smallest, significant quality loss - not recommended for most purposes |
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| [Gemma-2-9B-Chinese-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q3_K_L.gguf) | Q3_K_L | 3 | 5.13 GB| small, substantial quality loss |
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| [Gemma-2-9B-Chinese-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q3_K_M.gguf) | Q3_K_M | 3 | 4.76 GB| very small, high quality loss |
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| [Gemma-2-9B-Chinese-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q3_K_S.gguf) | Q3_K_S | 3 | 4.34 GB| very small, high quality loss |
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| [Gemma-2-9B-Chinese-Chat-Q4_0.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q4_0.gguf) | Q4_0 | 4 | 5.44 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
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| [Gemma-2-9B-Chinese-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q4_K_M.gguf) | Q4_K_M | 4 | 5.76 GB| medium, balanced quality - recommended |
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| [Gemma-2-9B-Chinese-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q4_K_S.gguf) | Q4_K_S | 4 | 5.48 GB| small, greater quality loss |
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| [Gemma-2-9B-Chinese-Chat-Q5_0.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q5_0.gguf) | Q5_0 | 5 | 6.48 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
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| [Gemma-2-9B-Chinese-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q5_K_M.gguf) | Q5_K_M | 5 | 6.65 GB| large, very low quality loss - recommended |
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| [Gemma-2-9B-Chinese-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q5_K_S.gguf) | Q5_K_S | 5 | 6.48 GB| large, low quality loss - recommended |
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| [Gemma-2-9B-Chinese-Chat-Q6_K.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q6_K.gguf) | Q6_K | 6 | 7.59 GB| very large, extremely low quality loss |
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| [Gemma-2-9B-Chinese-Chat-Q8_0.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-Q8_0.gguf) | Q8_0 | 8 | 9.83 GB| very large, extremely low quality loss - not recommended |
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| [Gemma-2-9B-Chinese-Chat-f16.gguf](https://huggingface.co/second-state/Gemma-2-9B-Chinese-Chat-GGUF/blob/main/Gemma-2-9B-Chinese-Chat-f16.gguf) | f16 | 16 | 18.5 GB| |
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*Quantized with llama.cpp b3259*
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config.json
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{
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"_name_or_path": "shenzhi-wang/Gemma-2-9B-Chinese-Chat",
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 224,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.2",
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"use_cache": false,
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"vocab_size": 256000
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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