Instructions to use trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528") model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528") 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
- vLLM
How to use trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528
- SGLang
How to use trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528 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 "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528" \ --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": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528", "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 "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528" \ --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": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528 with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528
Upload DeepseekV3ForCausalLM
#2
by qgallouedec HF Staff - opened
- config.json +31 -8
- generation_config.json +1 -1
- model.safetensors +2 -2
config.json
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"first_k_dense_replace": 3,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size":
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"initializer_range": 0.02,
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"intermediate_size":
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"kv_lora_rank":
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"max_position_embeddings":
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"model_type": "deepseek_v3",
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"moe_intermediate_size": 2048,
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"n_group": 8,
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"n_routed_experts": 256,
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"n_shared_experts": 1,
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"num_experts_per_tok": 8,
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"num_hidden_layers": 2,
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"num_key_value_heads": 2,
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"pretraining_tp": 1,
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"q_lora_rank":
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"qk_head_dim": 192,
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"qk_nope_head_dim": 128,
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"qk_rope_head_dim": 64,
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"rms_norm_eps": 1e-06,
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"rope_interleave": true,
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"rope_scaling":
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"rope_theta": 10000.0,
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"routed_scaling_factor": 2.5,
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"tie_word_embeddings": false,
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"topk_group": 4,
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"
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"use_cache": true,
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"v_head_dim": 128,
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"vocab_size":
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}
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"ep_size": 1,
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"first_k_dense_replace": 3,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 128,
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"initializer_range": 0.02,
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"intermediate_size": 128,
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"kv_lora_rank": 128,
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"max_position_embeddings": 163840,
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"model_type": "deepseek_v3",
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"moe_intermediate_size": 2048,
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"moe_layer_freq": 1,
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"n_group": 8,
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"n_routed_experts": 256,
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"n_shared_experts": 1,
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"num_experts_per_tok": 8,
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"num_hidden_layers": 2,
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"num_key_value_heads": 2,
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"num_nextn_predict_layers": 1,
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"pretraining_tp": 1,
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"q_lora_rank": 128,
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"qk_head_dim": 192,
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"qk_nope_head_dim": 128,
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"qk_rope_head_dim": 64,
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"quantization_config": {
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"activation_scheme": "dynamic",
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"fmt": "e4m3",
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"quant_method": "fp8",
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"weight_block_size": [
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128,
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128
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]
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},
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"rms_norm_eps": 1e-06,
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"rope_interleave": true,
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"rope_scaling": {
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"beta_fast": 32.0,
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"beta_slow": 1.0,
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"factor": 40.0,
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"mscale": 1.0,
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"mscale_all_dim": 1.0,
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"original_max_position_embeddings": 4096,
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"rope_type": "yarn",
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"type": "yarn"
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},
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"rope_theta": 10000.0,
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"routed_scaling_factor": 2.5,
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"scoring_func": "sigmoid",
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"tie_word_embeddings": false,
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"topk_group": 4,
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"topk_method": "noaux_tc",
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"transformers_version": "4.56.2",
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"use_cache": true,
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"v_head_dim": 128,
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"vocab_size": 129280
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}
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generation_config.json
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"eos_token_id": 1,
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"temperature": 0.6,
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"top_p": 0.95,
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"transformers_version": "4.
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}
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"eos_token_id": 1,
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"temperature": 0.6,
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"top_p": 0.95,
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"transformers_version": "4.56.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:c151d7a9bc82a83de81dae611ec26e839aae67f878a89130440870632f95c337
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size 66968648
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