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README.md

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+# Lightweight Deepseek R1 (3 Hidden Layers Version)
+
+This project is created using the official **Deepseek R1** model script (`modeling_deepseek.py`) from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1/blob/main/modeling_deepseek.py). It implements a **3-layer version** of Deepseek R1 with randomly initialized weights.
+
+## Model Structure
+The three hidden layers consist of:
+- **A hidden layer using Dense MLP**
+- **A hidden layer using MoE (Mixture of Experts) as MLP part**
+- **A MTP (Multi-Token Pretraining) layer**
+
+## Purpose
+The purpose of these weights is to provide a lightweight implementation for researchers who want to study the model architecture and run experiments quickly.
+
+The original **Deepseek R1 model** requires an **8x H200 GPU setup** and runs on the **vLLM/SGLang framework**, making it difficult to deploy on standard hardware.
+
+## Usage
+
+```python
+from transformers import AutoConfig, AutoModelForCausalLM
+from transformers import AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained('silence09/DeepSeek-R1-3layers', torch_dtype=torch.bfloat16).cuda()
+tokenizer = AutoTokenizer.from_pretrained('silence09/DeepSeek-R1-3layers')
+
+prompt = "Who are u?"
+messages = []
+messages.append({"role": "user", "content": prompt})
+prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+generated_ids = model.generate(prompt_tokens, max_new_tokens=100, do_sample=False)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(prompt_tokens, generated_ids)
+]
+completion = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(completion)
+messages.append({"role": "assistant", "content": completion})
+
+```
+
+## More Info
+It was created using the python script available at [this repository](https://github.com/silencelamb/naked_llama/blob/main/hf_example/create_deepseek_r1_3layers.py)
+

config.json

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+{
+  "_name_or_path": "deepseek-ai/DeepSeek-R1",
+  "architectures": [
+    "DeepseekV3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "deepseek-ai/DeepSeek-R1--configuration_deepseek.DeepseekV3Config",
+    "AutoModel": "deepseek-ai/DeepSeek-R1--modeling_deepseek.DeepseekV3Model",
+    "AutoModelForCausalLM": "deepseek-ai/DeepSeek-R1--modeling_deepseek.DeepseekV3ForCausalLM"
+  },
+  "aux_loss_alpha": 0.001,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 1,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 18432,
+  "kv_lora_rank": 512,
+  "max_position_embeddings": 163840,
+  "model_type": "deepseek_v3",
+  "moe_intermediate_size": 2048,
+  "moe_layer_freq": 1,
+  "n_group": 8,
+  "n_routed_experts": 256,
+  "n_shared_experts": 1,
+  "norm_topk_prob": true,
+  "num_attention_heads": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 3,
+  "num_key_value_heads": 128,
+  "num_nextn_predict_layers": 1,
+  "pretraining_tp": 1,
+  "q_lora_rank": 1536,
+  "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": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "type": "yarn"
+  },
+  "rope_theta": 10000,
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sigmoid",
+  "seq_aux": true,
+  "tie_word_embeddings": false,
+  "topk_group": 4,
+  "topk_method": "noaux_tc",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "v_head_dim": 128,
+  "vocab_size": 129280
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "transformers_version": "4.48.2"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}