GGUF
Spanish
English
deepseek_v2
moderno-tech
nova
nova-coder
quantized
code-generation
local-ai
custom_code
conversational
Instructions to use ModernoTech/Nova-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ModernoTech/Nova-Coder with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ModernoTech/Nova-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ModernoTech/Nova-Coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ModernoTech/Nova-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ModernoTech/Nova-Coder:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf ModernoTech/Nova-Coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf ModernoTech/Nova-Coder:Q4_K_M
Use Docker
docker model run hf.co/ModernoTech/Nova-Coder:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use ModernoTech/Nova-Coder with Ollama:
ollama run hf.co/ModernoTech/Nova-Coder:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ModernoTech/Nova-Coder with Docker Model Runner:
docker model run hf.co/ModernoTech/Nova-Coder:Q4_K_M
- Lemonade
How to use ModernoTech/Nova-Coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ModernoTech/Nova-Coder:Q4_K_M
Run and chat with the model
lemonade run user.Nova-Coder-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Commit ·
22c1157
0
Parent(s):
Duplicate from second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF
Browse filesCo-authored-by: Sam Liu <apepkuss79@users.noreply.huggingface.co>
- .gitattributes +48 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q2_K.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q3_K_L.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q3_K_M.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q3_K_S.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q4_0.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q4_K_S.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q5_0.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q5_K_S.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q6_K.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-Q8_0.gguf +3 -0
- DeepSeek-Coder-V2-Lite-Instruct-f16.gguf +3 -0
- README.md +88 -0
- config.json +59 -0
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README.md
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---
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base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
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inference: false
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license: other
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license_name: deepseek-license
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license_link: LICENSE
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model_creator: DeepSeek-ai
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model_name: DeepSeek-Coder-V2-Lite-Instruct
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model_type: deepseek
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quantized_by: Second State Inc.
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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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# DeepSeek-Coder-V2-Lite-Instruct-GGUF
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## Original Model
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[deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)
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## Run with LlamaEdge
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- LlamaEdge version: [v0.12.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.12.3) and above
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- Prompt template
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- Prompt type: `deepseek-chat-2`
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- Prompt string
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```text
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<|begin_of_sentence|>{system_message}
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User: {user_message_1}
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Assistant: {assistant_message_1}<|end_of_sentence|>User: {user_message_2}
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Assistant:
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```
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- Context size: `128000`
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- Run as LlamaEdge service
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```bash
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wasmedge --dir .:. \
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--nn-preload default:GGML:AUTO:DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf \
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llama-api-server.wasm \
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--prompt-template deepseek-chat-2 \
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--ctx-size 128000 \
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--model-name DeepSeek-Coder-V2-Lite-Instruct
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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:DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf \
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llama-chat.wasm \
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--prompt-template deepseek-chat-2 \
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--ctx-size 128000
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```
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## Quantized GGUF Models
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| 71 |
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| Name | Quant method | Bits | Size | Use case |
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| 73 |
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| ---- | ---- | ---- | ---- | ----- |
|
| 74 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q2_K.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q2_K.gguf) | Q2_K | 2 | 6.43 GB| smallest, significant quality loss - not recommended for most purposes |
|
| 75 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 8.46 GB| small, substantial quality loss |
|
| 76 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 8.13 GB| very small, high quality loss |
|
| 77 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 7.49 GB| very small, high quality loss |
|
| 78 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q4_0.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q4_0.gguf) | Q4_0 | 4 | 8.91 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
|
| 79 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 10.4 GB| medium, balanced quality - recommended |
|
| 80 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 9.53 GB| small, greater quality loss |
|
| 81 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q5_0.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q5_0.gguf) | Q5_0 | 5 | 10.8 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
|
| 82 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 11.9 GB| large, very low quality loss - recommended |
|
| 83 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 11.1 GB| large, low quality loss - recommended |
|
| 84 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q6_K.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q6_K.gguf) | Q6_K | 6 | 14.1 GB| very large, extremely low quality loss |
|
| 85 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-Q8_0.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-Q8_0.gguf) | Q8_0 | 8 | 16.7 GB| very large, extremely low quality loss - not recommended |
|
| 86 |
+
| [DeepSeek-Coder-V2-Lite-Instruct-f16.gguf](https://huggingface.co/second-state/DeepSeek-Coder-V2-Lite-Instruct-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Instruct-f16.gguf) | f16 | 16 | 31.4 GB| |
|
| 87 |
+
|
| 88 |
+
*Quatized with llama.cpp b3212*
|
config.json
ADDED
|
@@ -0,0 +1,59 @@
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"DeepseekV2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_deepseek.DeepseekV2Config",
|
| 9 |
+
"AutoModel": "modeling_deepseek.DeepseekV2Model",
|
| 10 |
+
"AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
|
| 11 |
+
},
|
| 12 |
+
"aux_loss_alpha": 0.001,
|
| 13 |
+
"bos_token_id": 100000,
|
| 14 |
+
"eos_token_id": 100001,
|
| 15 |
+
"first_k_dense_replace": 1,
|
| 16 |
+
"hidden_act": "silu",
|
| 17 |
+
"hidden_size": 2048,
|
| 18 |
+
"initializer_range": 0.02,
|
| 19 |
+
"intermediate_size": 10944,
|
| 20 |
+
"kv_lora_rank": 512,
|
| 21 |
+
"max_position_embeddings": 163840,
|
| 22 |
+
"model_type": "deepseek_v2",
|
| 23 |
+
"moe_intermediate_size": 1408,
|
| 24 |
+
"moe_layer_freq": 1,
|
| 25 |
+
"n_group": 1,
|
| 26 |
+
"n_routed_experts": 64,
|
| 27 |
+
"n_shared_experts": 2,
|
| 28 |
+
"norm_topk_prob": false,
|
| 29 |
+
"num_attention_heads": 16,
|
| 30 |
+
"num_experts_per_tok": 6,
|
| 31 |
+
"num_hidden_layers": 27,
|
| 32 |
+
"num_key_value_heads": 16,
|
| 33 |
+
"pretraining_tp": 1,
|
| 34 |
+
"q_lora_rank": null,
|
| 35 |
+
"qk_nope_head_dim": 128,
|
| 36 |
+
"qk_rope_head_dim": 64,
|
| 37 |
+
"rms_norm_eps": 1e-06,
|
| 38 |
+
"rope_scaling": {
|
| 39 |
+
"beta_fast": 32,
|
| 40 |
+
"beta_slow": 1,
|
| 41 |
+
"factor": 40,
|
| 42 |
+
"mscale": 0.707,
|
| 43 |
+
"mscale_all_dim": 0.707,
|
| 44 |
+
"original_max_position_embeddings": 4096,
|
| 45 |
+
"type": "yarn"
|
| 46 |
+
},
|
| 47 |
+
"rope_theta": 10000,
|
| 48 |
+
"routed_scaling_factor": 1.0,
|
| 49 |
+
"scoring_func": "softmax",
|
| 50 |
+
"seq_aux": true,
|
| 51 |
+
"tie_word_embeddings": false,
|
| 52 |
+
"topk_group": 1,
|
| 53 |
+
"topk_method": "greedy",
|
| 54 |
+
"torch_dtype": "bfloat16",
|
| 55 |
+
"transformers_version": "4.39.3",
|
| 56 |
+
"use_cache": true,
|
| 57 |
+
"v_head_dim": 128,
|
| 58 |
+
"vocab_size": 102400
|
| 59 |
+
}
|