Instructions to use second-state/DeepSeek-V2-Lite-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="second-state/DeepSeek-V2-Lite-Chat-GGUF", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/DeepSeek-V2-Lite-Chat-GGUF", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("second-state/DeepSeek-V2-Lite-Chat-GGUF", trust_remote_code=True)

llama-cpp-python

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/DeepSeek-V2-Lite-Chat-GGUF",
	filename="DeepSeek-V2-Lite-Chat-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 second-state/DeepSeek-V2-Lite-Chat-GGUF 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 second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

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 second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

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 second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/DeepSeek-V2-Lite-Chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/DeepSeek-V2-Lite-Chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

SGLang

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF 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 "second-state/DeepSeek-V2-Lite-Chat-GGUF" \
    --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": "second-state/DeepSeek-V2-Lite-Chat-GGUF",
		"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 "second-state/DeepSeek-V2-Lite-Chat-GGUF" \
        --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": "second-state/DeepSeek-V2-Lite-Chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with Ollama:
```
ollama run hf.co/second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF 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 second-state/DeepSeek-V2-Lite-Chat-GGUF 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 second-state/DeepSeek-V2-Lite-Chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/DeepSeek-V2-Lite-Chat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M
```

Lemonade

How to use second-state/DeepSeek-V2-Lite-Chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/DeepSeek-V2-Lite-Chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-V2-Lite-Chat-GGUF-Q4_K_M

List all available models

lemonade list

DeepSeek-V2-Lite-Chat-GGUF

File size: 1,522 Bytes

9d72d01

{
  "architectures": [
    "DeepseekV2ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_deepseek.DeepseekV2Config",
    "AutoModel": "modeling_deepseek.DeepseekV2Model",
    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
  },
  "aux_loss_alpha": 0.001,
  "bos_token_id": 100000,
  "eos_token_id": 100001,
  "first_k_dense_replace": 1,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 10944,
  "kv_lora_rank": 512,
  "max_position_embeddings": 163840,
  "model_type": "deepseek_v2",
  "moe_intermediate_size": 1408,
  "moe_layer_freq": 1,
  "n_group": 1,
  "n_routed_experts": 64,
  "n_shared_experts": 2,
  "norm_topk_prob": false,
  "num_attention_heads": 16,
  "num_experts_per_tok": 6,
  "num_hidden_layers": 27,
  "num_key_value_heads": 16,
  "pretraining_tp": 1,
  "q_lora_rank": null,
  "qk_nope_head_dim": 128,
  "qk_rope_head_dim": 64,
  "rms_norm_eps": 1e-06,
  "rope_scaling": {
    "beta_fast": 32,
    "beta_slow": 1,
    "factor": 40,
    "mscale": 0.707,
    "mscale_all_dim": 0.707,
    "original_max_position_embeddings": 4096,
    "type": "yarn"
  },
  "rope_theta": 10000,
  "routed_scaling_factor": 1.0,
  "scoring_func": "softmax",
  "seq_aux": true,
  "tie_word_embeddings": false,
  "topk_group": 1,
  "topk_method": "greedy",
  "torch_dtype": "bfloat16",
  "transformers_version": "4.33.1",
  "use_cache": true,
  "v_head_dim": 128,
  "vocab_size": 102400
}