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README.md

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+# Converted LLaMA from InternLM2.5-7B-Chat
+
+## Descritpion
+This is a converted model from [InternLM2.5-7B-Chat](https://huggingface.co/internlm/internlm2_5-7b-chat) to __LLaMA__ format. This conversion allows you to use InternLM2.5-7B-Chat as if it were a LLaMA model, which is convenient for some *inference use cases*. The __precision__ is __excatly the same__ as the original model. 
+
+## Usage
+You can load the model using the `LlamaForCausalLM` class as shown below:
+```python
+device = "cpu" # cpu is exacatly the same
+attn_impl = 'eager' # the attention implementation to use
+meta_instruction = ("You are an AI assistant whose name is InternLM (书生·浦语).\n"
+"- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
+"(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+"- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such "
+"as English and 中文."
+)
+prompt1 = "介绍下你自己"
+prompt2 = "介绍下上海人工智能实验室"
+
+def build_inputs(tokenizer, query: str, history: List[Tuple[str, str]] = None, meta_instruction=meta_instruction):
+    if history is None:
+        history = []
+    if tokenizer.add_bos_token:
+        prompt = ""
+    else:
+        prompt = tokenizer.bos_token
+    if meta_instruction:
+        prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+    for record in history:
+        prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+    prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+    return tokenizer([prompt], return_tensors="pt")
+
+@torch.inference_mode()
+def chat(
+    model: Union[AutoModelForCausalLM, LlamaForCausalLM],
+    tokenizer,
+    query: str,
+    history: Optional[List[Tuple[str, str]]] = None,
+    streamer: Optional[BaseStreamer] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = True,
+    temperature: float = 0.8,
+    top_p: float = 0.8,
+    meta_instruction: str = meta_instruction,
+    **kwargs,
+):
+    if history is None:
+        history = []
+    inputs = build_inputs(tokenizer, query, history, meta_instruction)
+    inputs = {k: v.to(model.device) for k, v in inputs.items() if torch.is_tensor(v)}
+    # also add end-of-assistant token in eos token id to avoid unnecessary generation
+    eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
+    outputs = model.generate(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        temperature=temperature,
+        top_p=top_p,
+        eos_token_id=eos_token_id,
+        **kwargs,
+    )
+    outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
+    response = tokenizer.decode(outputs, skip_special_tokens=True)
+    response = response.split("<|im_end|>")[0]
+    history = history + [(query, response)]
+    return response, history
+    
+
+# use the official tokenizer
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2_5-7b-chat", trust_remote_code=True)
+# use the converted LlaMA model
+llama_model = LlamaForCausalLM.from_pretrained(
+    "silence09/InternLM2.5-7B-Chat-Converted-LlaMA",
+    torch_dtype='auto',
+    attn_implementation=attn_impl).to(device)
+llama_model.eval()
+response_llama_and_splitfunc_1, history = chat(llama_model, tokenizer, prompt1, history=[], do_sample=False)
+print(f"User Input: {prompt1}\nConverted LlaMA Response: {response_llama_and_splitfunc_1}")
+
+response_llama_and_splitfunc_2, history = chat(llama_model, tokenizer, prompt2, history=history, do_sample=False)
+print(f"User Input: {prompt2}\nConverted LlaMA Response: {response_llama_and_splitfunc_2}")
+
+```
+
+## Precision Guarantee
+To comare result with the original model, you can use this [code](https://github.com/silencelamb/naked_llama/blob/main/hf_example/hf_internlm_7b.py)
+
+## More Info
+It was converted using the python script available at [this repository](https://github.com/silencelamb/naked_llama/blob/main/hf_example/convert_internlm_to_llama_hf.py)
+

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "rope_type": "dynamic",
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 92544
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    92542
+  ],
+  "pad_token_id": 2,
+  "transformers_version": "4.44.2"
+}

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