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README.md

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+
+---
+
+library_name: transformers
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: Hello!
+  example_title: Hello world
+  group: Python
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/deepseek-v3-tiny-random-GGUF
+This is quantized version of [yujiepan/deepseek-v3-tiny-random](https://huggingface.co/yujiepan/deepseek-v3-tiny-random) created using llama.cpp
+
+# Original Model Card
+
+
+This model is for debugging. It is randomly initialized with the config from [deepseek-ai/DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3) but is of smaller size. 
+
+**⚠️Note: At this moment, this repo does not contain the Multi-Token Prediction (MTP) module as explained [here](https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/README_WEIGHTS.md).**
+
+Usage:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "yujiepan/deepseek-v3-tiny-random"
+device = torch.device("cuda")
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, trust_remote_code=True,
+).eval().to(device)
+
+prompt = 'Hello!'
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
+).to(device)
+
+with torch.inference_mode():
+    outputs = model.generate(
+        inputs,
+        max_new_tokens=16,
+        do_sample=False,
+        use_cache=True,
+    )
+string = tokenizer.decode(outputs[0])
+print(string)
+```
+
+Codes:
+```python
+import os
+from pathlib import Path
+
+import torch
+import transformers
+from huggingface_hub import create_repo, upload_folder
+from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
+                          GenerationConfig, enable_full_determinism, pipeline,
+                          set_seed)
+
+model_id = "deepseek-ai/DeepSeek-V3"
+repo_id = "yujiepan/deepseek-v3-tiny-random"
+save_path = f"/tmp/{repo_id}"
+os.system(f"rm -rf {save_path}")
+
+config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+config.num_hidden_layers = 2
+config.first_k_dense_replace = 1
+config.hidden_size = 16
+config.intermediate_size = 32
+config.moe_intermediate_size = 16
+config.q_lora_rank = 16
+config.kv_lora_rank = 16
+config.qk_rope_head_dim = 16
+config.qk_nope_head_dim = 16
+config.v_head_dim = 16
+config.num_attention_heads = 2
+config.num_key_value_heads = 2
+# transformers has not supported the customized quantization config
+del config.quantization_config
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+tokenizer.save_pretrained(save_path)
+
+enable_full_determinism(seed=42)
+model = AutoModelForCausalLM.from_config(
+    config, torch_dtype=torch.bfloat16, trust_remote_code=True,
+).eval()
+
+try:
+    model.generation_config = GenerationConfig.from_pretrained(
+        model_id, trust_remote_code=True)
+except:
+    print("No generation config found")
+
+num_params = 0
+with torch.no_grad():
+    for name, p in sorted(model.named_parameters()):
+        if 'experts' in name and 'experts.0.' not in name:  # avoid printing too much
+            pass
+        else:
+            print(name, p.shape)
+        # torch.nn.init.uniform_(p, -0.2, 0.2)
+        num_params += p.numel()
+print(f"Number of parameters: {num_params / 1e6:.2f}M")
+model.save_pretrained(save_path)
+
+# patch to use official modeling codes
+auto_map = config.auto_map
+import json
+with open(f"{save_path}/config.json", "r") as f:
+    config = json.load(f)
+    config['auto_map'] = auto_map
+with open(f"{save_path}/config.json", "w") as f:
+    json.dump(config, f, indent=2)
+
+! cat {save_path}/config.json
+
+del model
+del tokenizer
+for p in Path(save_path).glob("*.py"):
+    os.remove(p)
+
+os.system(f"ls -alh {save_path}")
+torch.use_deterministic_algorithms(False)
+tokenizer = AutoTokenizer.from_pretrained(save_path)
+model = AutoModelForCausalLM.from_pretrained(
+    save_path, trust_remote_code=True).eval()
+prompt = 'Hello!'
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."}
+]
+messages.append({"role": "user", "content": prompt})
+tokenized_chat = tokenizer.apply_chat_template(
+    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+
+device = torch.device("cuda")
+outputs = model.to(device).generate(
+    tokenized_chat.to(device),
+    max_new_tokens=16,
+    do_sample=False,
+    use_cache=True,
+)
+tokens = tokenizer.convert_ids_to_tokens(outputs[0])
+string = tokenizer.decode(outputs[0])
+print(tokens)
+
+
+# create_repo(repo_id, exist_ok=True)
+# upload_folder(repo_id=repo_id, folder_path=save_path)
+```
+