DeepSeek-V2-Lite
By deepseek-ai. 15.7 billion parameters, a context window of 163,840 tokens and the licence other.
Facts
- Released
- 2024-05-15 the day the repository was first published on Hugging Face Hugging Face, read
- Licence
- other the licence the model card declares Hugging Face, read
- Open weights
- Yes the weights are published in this Hugging Face repository Hugging Face, read
- Parameters
- 15.7 billion counted from the safetensors weight files Hugging Face, read
- Active parameters
- 2.7 billion estimate estimated from the expert counts and sizes in the configuration model configuration, read
- Knowledge cutoff
- Unknown
- Context window
- 163,840 tokens the maximum position embeddings in the model configuration model configuration, read
- Longest output
- Unknown
- Tool calling
- No the chat template in the tokenizer configuration has no place for tool definitions Hugging Face, read
- Structured output
- Unknown
- Reasoning controls
- Unknown
- Inputs and outputs
- text in, text out Hugging Face, read
- Good for
- No source names a use.
Prices
No source lists a price for this model. It may only run on your own hardware.
Run it on your own hardware
Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.
| Quantization | Weights | Memory at 8,192 tokens | Memory at 32,768 tokens |
|---|---|---|---|
| IQ1_S | 4.7 GiB | 5.6 GiB | 6.3 GiB |
| IQ1_M | 4.9 GiB | 5.9 GiB | 6.6 GiB |
| IQ2_XXS | 5.3 GiB | 6.3 GiB | 7.0 GiB |
| IQ2_XS | 5.6 GiB | 6.6 GiB | 7.3 GiB |
| IQ2_S | 5.6 GiB | 6.6 GiB | 7.3 GiB |
| IQ2_M | 5.9 GiB | 6.9 GiB | 7.6 GiB |
| Q2_K | 6.0 GiB | 7.0 GiB | 7.7 GiB |
| Q2_K_S | 6.0 GiB | 7.0 GiB | 7.8 GiB |
| IQ3_XXS | 6.5 GiB | 7.5 GiB | 8.3 GiB |
| IQ3_XS | 6.6 GiB | 7.7 GiB | 8.4 GiB |
| IQ3_S | 7.0 GiB | 8.1 GiB | 8.8 GiB |
| Q3_K_S | 7.0 GiB | 8.1 GiB | 8.8 GiB |
| IQ3_M | 7.0 GiB | 8.1 GiB | 8.8 GiB |
| Q3_K | 7.6 GiB | 8.7 GiB | 9.4 GiB |
| Q3_K_L | 7.9 GiB | 9.0 GiB | 9.7 GiB |
| IQ4_XS | 8.0 GiB | 9.1 GiB | 9.8 GiB |
| IQ4_NL | 8.3 GiB | 9.4 GiB | 10.2 GiB |
| Q4_K_S | 8.9 GiB | 10.1 GiB | 10.8 GiB |
| Q4_K | 9.7 GiB | 10.9 GiB | 11.6 GiB |
| Q5_K_S | 10.4 GiB | 11.6 GiB | 12.3 GiB |
| Q5_K | 11.0 GiB | 12.3 GiB | 13.0 GiB |
| Q6_K | 13.1 GiB | 14.5 GiB | 15.2 GiB |
| Q8_0 | 15.6 GiB | 17.1 GiB | 17.8 GiB |
| BF16 | 29.3 GiB | 31.5 GiB | 32.2 GiB |
Start it with a local runtime
- Ollama
ollama run hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S - LM Studio
lms get legraphista/DeepSeek-V2-Lite-IMat-GGUF lms server start - llama.cpp server
llama-server -hf legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S --jinja - vLLM
vllm serve deepseek-ai/DeepSeek-V2-Lite - SGLang
sglang serve --model-path deepseek-ai/DeepSeek-V2-Lite --port 30000
Use it from your harness
Set up for Ollama with the model hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S. Each endpoint page has the same setup for its own address.
OpenCode
Put this in opencode.json in your project folder:
{
"$schema": "https://opencode.ai/config.json",
"provider": {
"ollama": {
"npm": "@ai-sdk/openai-compatible",
"name": "Ollama",
"options": {
"baseURL": "http://localhost:11434/v1"
},
"models": {
"hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S": {
"name": "DeepSeek-V2-Lite"
}
}
}
}
}- OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.
From OpenCode documentation, read .
Pi
Put this in ~/.pi/agent/models.json:
{
"providers": {
"ollama": {
"baseUrl": "http://localhost:11434/v1",
"api": "openai-completions",
"apiKey": "ollama",
"models": [
{
"id": "hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S"
}
]
}
}
}- The apiKey field can name an environment variable as $NAME.
From Pi documentation, read .
Codex
Put this in a terminal:
codex --oss --local-provider ollama -m hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S- Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.
From Codex documentation, read .
Claude Code
Put this in ~/.claude/settings.json, or variables in your shell:
export ANTHROPIC_BASE_URL="http://localhost:11434"
export ANTHROPIC_AUTH_TOKEN="ollama"
export ANTHROPIC_MODEL="hf.co/legraphista/DeepSeek-V2-Lite-IMat-GGUF:Q4_K_S"
claude- Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.
From Claude Code documentation, read .
Published results
These are results other people published. Baltor did not run them and does not rank models by them.
- Results the maker published on the model card published by deepseek-ai Hugging Face, read
Sources of this page
Paid links
No link in this directory is a paid link or an ad, and no listing is paid for. The order and the contents of every list come from the sources named on this page.