初始化项目，由ModelHub XC社区提供模型

Model: Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
Source: Original Platform

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README.md

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+---
+license: llama2
+datasets:
+- Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized
+- >-
+  Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized
+language:
+- te
+- en
+---
+# Telugu-Llama2-7B-v0-Instruct
+
+
+This model is based on [Telugu-Llama2-7B-v0-Base](https://huggingface.co/Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Base) and hase been finetuned on instruction datasets:
+  1. [yahma_alpaca_cleaned_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized)
+  2. [teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized)
+
+# Input Text Format
+
+```
+### Instruction: {instruction}
+
+### Input: {input}
+
+## Response: {response}
+```
+
+# Usage
+
+## With Romanized Telugu
+
+```python3
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right")
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
+
+instruction = "Krindi samaacharam prakaram google app eppudu release ayyindi?"
+input ="Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006."
+
+text = f"""Instruction: {instruction} \nInput: {input} \nResponse:"""
+
+encodings = tokenizer(text, padding=True, return_tensors="pt")
+encodings = encodings.to(device)
+
+with torch.inference_mode():
+    outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500)
+
+output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
+```
+
+### Sample Output:
+
+```
+1. September 2002 Google released a beta version of Google News.
+2. January 2006 Google released the official version of Google News.
+```
+
+## With Native Telugu
+
+```python3
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right")
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
+
+instruction = "కింది వచనాన్ని సంగ్రహించండి"
+input="గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ. ఇది వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. గూగుల్ వార్తలు Android, iOS మరియు వెబ్‌లో యాప్‌గా అందుబాటులో ఉన్నాయి. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్‌ను మరియు జనవరి 2006లో అధికారిక యాప్‌ను విడుదల చేసింది."
+
+text = f"""Instruction: {instruction} \nInput: {input} \nResponse:"""
+
+encodings = tokenizer(text, padding=True, return_tensors="pt")
+encodings = encodings.to(device)
+
+with torch.inference_mode():
+    outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500)
+
+output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
+```
+
+### Sample Output:
+
+1. గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ, వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది.
+2. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్ మరియు జనవరి 2006లో అధికారిక యాప్ ను విడుదల చేసింది.
+
+# Developers:
+
+The model is a collaborative effort by [Ravi Theja](https://twitter.com/ravithejads) and [Ramsri Goutham](https://twitter.com/ramsri_goutham). Feel free to DM either of us if you have any questions.
+
+# Note:
+
+The model is quite sensitive to parameters and inputs and is not yet ready for production. It remains in the experimental phase, and we recommend using it accordingly.

config.json

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+{
+  "_name_or_path": "ravithejads/telugumodels",
+  "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": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.37.0"
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+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,42 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}