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Model: mesolitica/malaysian-mistral-7b-32k-instructions
Source: Original Platform

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README.md

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+---
+language:
+- ms
+---
+
+# Full Parameter Finetuning 7B 32768 context length Mistral on Malaysian instructions dataset
+
+README at https://github.com/mesolitica/malaya/tree/5.1/session/mistral#instructions-7b-16384-context-length
+
+We use exact Mistral Instruct chat template.
+
+WandB, https://wandb.ai/mesolitica/fpf-mistral-7b-hf-instructions-16k?workspace=user-husein-mesolitica
+
+## how-to
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+import json
+
+def parse_mistral_chat(messages, function_call = None):
+
+    user_query = messages[-1]['content']
+
+    users, assistants = [], []
+    for q in messages[:-1]:
+        if q['role'] == 'user':
+            users.append(q['content'])
+        elif q['role'] == 'assistant':
+            assistants.append(q['content'])
+
+    texts = ['<s>']
+    
+    if function_call:
+        fs = []
+        for f in function_call:
+            f = json.dumps(f, indent=4)
+            fs.append(f)
+        fs = '\n\n'.join(fs)
+        texts.append(f'\n[FUNCTIONCALL]\n{fs}\n')
+        
+    for u, a in zip(users, assistants):
+        texts.append(f'[INST] {u.strip()} [/INST] {a.strip()}</s>')
+
+    texts.append(f'[INST] {user_query.strip()} [/INST]')
+    prompt = ''.join(texts).strip()
+    return prompt
+
+TORCH_DTYPE = 'bfloat16'
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
+)
+
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-mistral-7b-32k-instructions')
+model = AutoModelForCausalLM.from_pretrained(
+    'mesolitica/malaysian-mistral-7b-32k-instructions',
+    use_flash_attention_2 = True,
+    quantization_config = nf4_config
+)
+
+messages = [
+    {'role': 'user', 'content': 'kwsp tu apa'}
+]
+prompt = parse_mistral_chat(messages)
+inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+generate_kwargs = dict(
+    inputs,
+    max_new_tokens=1024,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.9,
+    do_sample=True,
+    num_beams=1,
+)
+r = model.generate(**generate_kwargs)
+tokenizer.decode(r[0])
+```
+
+```text
+<s> [INST] kwsp tu apa [/INST]KWSP bermaksud Kumpulan Wang Simpanan Pekerja. Ia adalah sebuah institusi simpanan persaraan yang ditubuhkan oleh Kementerian Kewangan Malaysia untuk tujuan mengumpul simpanan ahli untuk dibayar pada umur persaraan, penuh atau penuh persaraan penuh. KWSP ditubuhkan pada tahun 1951 dan mula beroperasi pada tahun 1952. KWSP adalah salah satu institusi simpanan persaraan terbesar di dunia, dengan pangkalan ahli sekitar 14 juta ahli.</s>
+```
+
+```python
+messages = [
+    {'role': 'user', 'content': 'camne nak kill pid dalam linux'}
+]
+prompt = parse_mistral_chat(messages)
+inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+generate_kwargs = dict(
+    inputs,
+    max_new_tokens=1024,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.9,
+    do_sample=True,
+    num_beams=1,
+)
+r = model.generate(**generate_kwargs)
+print(tokenizer.decode(r[0]))
+```
+
+```text
+<s> [INST] camne nak kill pid dalam linux [/INST]Untuk membunuh pid dalam Linux, ikuti langkah-langkah berikut:
+
+1. Cari pid menggunakan perintah `ps aux | grep 'pid'`. Pilih pid yang anda ingin bunuh.
+
+2. Gunakan perintah `kill` diikuti dengan nombor pid. Sebagai contoh, jika anda ingin membunuh pid dengan nombor 1234, gunakan perintah `kill -1234`.
+
+3. Sahkan pembunuhan dengan menjalankan perintah `kill -id`. Jika perintah tidak mengembalikan sebarang ralat, maka pid telah dibunuh dengan berjaya.
+
+4. Anda juga boleh menggunakan perintah `kill -s` untuk membunuh semua pid dengan nama atau atribut tertentu.</s>
+```
+
+```python
+f = {
+    'name': 'parse_entities',
+    'description': 'extract entities from the text',
+    'parameters': {
+        'type': 'object',
+        'properties': {
+            'drink': {
+                'type': 'string',
+                'description': 'drink name',
+            },
+            'event': {
+                'type': 'string',
+                'description': 'event name',
+            },
+            'person_name': {
+                'type': 'string',
+                'description': 'person name',
+            }
+        },
+        'required': [
+            'drink',
+            'event',
+            'person_name'
+        ]
+    }
+}
+messages = [
+    {'role': 'user', 'content': 'nama saya husein bin zolkepli, saya sekarang berada di jomheboh 2023 sambil minum starbucks'}
+]
+prompt = parse_mistral_chat(messages, function_call = [f])
+inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+generate_kwargs = dict(
+    inputs,
+    max_new_tokens=128,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.9,
+    do_sample=True,
+    num_beams=1,
+)
+r = model.generate(**generate_kwargs)
+print(tokenizer.decode(r[0]))
+```
+
+```text
+<s> 
+[FUNCTIONCALL]
+{
+    "name": "parse_entities",
+    "description": "extract entities from the text",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "drink": {
+                "type": "string",
+                "description": "drink name"
+            },
+            "event": {
+                "type": "string",
+                "description": "event name"
+            },
+            "person_name": {
+                "type": "string",
+                "description": "person name"
+            }
+        },
+        "required": [
+            "drink",
+            "event",
+            "person_name"
+        ]
+    }
+}
+[INST] nama saya husein bin zolkepli, saya sekarang berada di jomheboh 2023 sambil minum starbucks [/INST] <functioncall> {"name": "parse_entities", "arguments": '{
+  "drink": "Starbucks",
+  "event": "Jom Heboh 2023",
+  "person_name": "Husein Bin Zolkepli"
+}'}</s>
+```

config.json

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+{
+  "_name_or_path": "fpf-7b-instructions-16k-call-v2/checkpoint-14100",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "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,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.35.0"
+}

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special_tokens_map.json

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tokenizer_config.json

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