Text Generation
Transformers
Safetensors
Rust
MLX
English
mini-deepseek-v4-flash
deepseek-v4
deepseek-v4-flash
coding-llm
code-generation
code-completion
programming
software-engineering
web-development
javascript
typescript
threejs
python
mixture-of-experts
Mixture of Experts
model-fusion
expert-routing
custom-architecture
trust-remote-code
long-context
bf16
fp4
nf4
int8
fp8
vllm
research
open-source
Instructions to use Akahsizrr/mini-deepseek-v4-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akahsizrr/mini-deepseek-v4-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Akahsizrr/mini-deepseek-v4-flash")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Akahsizrr/mini-deepseek-v4-flash", device_map="auto") - MLX
How to use Akahsizrr/mini-deepseek-v4-flash with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Akahsizrr/mini-deepseek-v4-flash") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use Akahsizrr/mini-deepseek-v4-flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Akahsizrr/mini-deepseek-v4-flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akahsizrr/mini-deepseek-v4-flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Akahsizrr/mini-deepseek-v4-flash
- SGLang
How to use Akahsizrr/mini-deepseek-v4-flash 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 "Akahsizrr/mini-deepseek-v4-flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akahsizrr/mini-deepseek-v4-flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Akahsizrr/mini-deepseek-v4-flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akahsizrr/mini-deepseek-v4-flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use Akahsizrr/mini-deepseek-v4-flash with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Akahsizrr/mini-deepseek-v4-flash" --prompt "Once upon a time"
- Docker Model Runner
How to use Akahsizrr/mini-deepseek-v4-flash with Docker Model Runner:
docker model run hf.co/Akahsizrr/mini-deepseek-v4-flash
File size: 466 Bytes
245e42e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"base_model": "Akahsizrr/Mini-Whale-Flash",
"base_subdir": "merged-v2-full",
"quantization": "bitsandbytes",
"bits": 4,
"quant_type": "nf4",
"compute_dtype": "bfloat16",
"gpu_validation": "NVIDIA A100-SXM4-40GB",
"runtime_fixes": {
"scaled_shared_experts": 303,
"clamped_experts": 303
},
"sample_output": "<think>\nOkay, the user wants me to say hello in one sentence. Let",
"parameter_count": 6397185536,
"elapsed_seconds": 47.6
}
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