Text Generation
Transformers
Safetensors
deepseek_v2
deepseek
fp8
quantized
compressed-tensors
llmcompressor
conversational
custom_code
text-generation-inference
Instructions to use Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", 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("Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded
- SGLang
How to use Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded 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 "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded" \ --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": "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", "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 "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded" \ --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": "Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded with Docker Model Runner:
docker model run hf.co/Etelis/DeepSeek-V2-Lite-FP8-BLOCK-padded
| { | |
| "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, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 100001, | |
| "ep_size": 1, | |
| "first_k_dense_replace": 1, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "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, | |
| "quantization_config": { | |
| "config_groups": { | |
| "group_0": { | |
| "format": "float-quantized", | |
| "input_activations": { | |
| "actorder": null, | |
| "block_structure": null, | |
| "dynamic": true, | |
| "group_size": 128, | |
| "num_bits": 8, | |
| "observer": null, | |
| "observer_kwargs": {}, | |
| "scale_dtype": null, | |
| "strategy": "group", | |
| "symmetric": true, | |
| "type": "float", | |
| "zp_dtype": null | |
| }, | |
| "output_activations": null, | |
| "targets": [ | |
| "Linear" | |
| ], | |
| "weights": { | |
| "actorder": null, | |
| "block_structure": [ | |
| 128, | |
| 128 | |
| ], | |
| "dynamic": false, | |
| "group_size": null, | |
| "num_bits": 8, | |
| "observer": "minmax", | |
| "observer_kwargs": {}, | |
| "scale_dtype": null, | |
| "strategy": "block", | |
| "symmetric": true, | |
| "type": "float", | |
| "zp_dtype": null | |
| } | |
| } | |
| }, | |
| "format": "float-quantized", | |
| "global_compression_ratio": null, | |
| "ignore": [ | |
| "lm_head" | |
| ], | |
| "kv_cache_scheme": null, | |
| "quant_method": "compressed-tensors", | |
| "quantization_status": "compressed", | |
| "sparsity_config": {}, | |
| "transform_config": {}, | |
| "version": "0.13.1.dev17+g012a74c" | |
| }, | |
| "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", | |
| "transformers_version": "4.57.6", | |
| "use_cache": true, | |
| "v_head_dim": 128, | |
| "vocab_size": 102400 | |
| } |