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--- |
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base_model: mesolitica/malaysian-tinyllama-1.1b-16k-instructions |
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inference: false |
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language: |
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- ms |
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model_creator: mesolitica |
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model_name: malaysian-tinyllama-1.1b-16k-instructions |
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pipeline_tag: text-generation |
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quantized_by: afrideva |
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tags: |
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- gguf |
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- ggml |
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- quantized |
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- q2_k |
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- q3_k_m |
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- q4_k_m |
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- q5_k_m |
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- q6_k |
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- q8_0 |
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--- |
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# mesolitica/malaysian-tinyllama-1.1b-16k-instructions-GGUF |
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Quantized GGUF model files for [malaysian-tinyllama-1.1b-16k-instructions](https://huggingface.co/mesolitica/malaysian-tinyllama-1.1b-16k-instructions) from [mesolitica](https://huggingface.co/mesolitica) |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q2_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q2_k.gguf) | q2_k | 482.14 MB | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q3_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q3_k_m.gguf) | q3_k_m | 549.85 MB | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q4_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q4_k_m.gguf) | q4_k_m | 667.81 MB | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q5_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q5_k_m.gguf) | q5_k_m | 782.04 MB | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q6_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q6_k.gguf) | q6_k | 903.41 MB | |
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| [malaysian-tinyllama-1.1b-16k-instructions.q8_0.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16k-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16k-instructions.q8_0.gguf) | q8_0 | 1.17 GB | |
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## Original Model Card: |
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# Full Parameter Finetuning TinyLlama 16384 context length on Malaysian instructions dataset |
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README at https://github.com/mesolitica/malaya/tree/5.1/session/tiny-llama#instructions-7b-16384-context-length |
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We use exact Llama2 Instruct chat template, added with function call |
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WandB, https://wandb.ai/mesolitica/fpf-tinyllama-1.1b-hf-instructions-16k-function-call?workspace=user-husein-mesolitica |
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## how-to |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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import torch |
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def parse_llama_chat(messages, function_call = None): |
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system = messages[0]['content'] |
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user_query = messages[-1]['content'] |
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users, assistants = [], [] |
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for q in messages[1:-1]: |
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if q['role'] == 'user': |
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users.append(q['content']) |
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elif q['role'] == 'assistant': |
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assistants.append(q['content']) |
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texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n'] |
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if function_call: |
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fs = [] |
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for f in function_call: |
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f = json.dumps(f, indent=4) |
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fs.append(f) |
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fs = '\n\n'.join(fs) |
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texts.append(f'\n[FUNCTIONCALL]\n{fs}\n') |
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for u, a in zip(users, assistants): |
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texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ') |
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texts.append(f'{user_query.strip()} [/INST]') |
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prompt = ''.join(texts).strip() |
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return prompt |
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TORCH_DTYPE = 'bfloat16' |
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nf4_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type='nf4', |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE) |
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) |
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tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-16k-instructions') |
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model = AutoModelForCausalLM.from_pretrained( |
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'mesolitica/malaysian-tinyllama-1.1b-16k-instructions', |
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use_flash_attention_2 = True, |
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quantization_config = nf4_config |
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) |
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messages = [ |
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{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'}, |
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{'role': 'user', 'content': 'kwsp tu apa'} |
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] |
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prompt = parse_llama_chat(messages) |
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inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') |
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generate_kwargs = dict( |
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inputs, |
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max_new_tokens=1024, |
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top_p=0.95, |
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top_k=50, |
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temperature=0.9, |
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do_sample=True, |
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num_beams=1, |
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) |
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r = model.generate(**generate_kwargs) |
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print(tokenizer.decode(r[0])) |
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``` |
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```text |
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'<s> [INST] <<SYS>> |
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awak adalah AI yang mampu jawab segala soalan |
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<</SYS>> |
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kwsp tu apa [/INST] KWSP bermaksud Kumpulan Wang Persaraan. </s>' |
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``` |
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```python |
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messages = [ |
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{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'}, |
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{'role': 'user', 'content': 'awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun'} |
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] |
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prompt = parse_llama_chat(messages) |
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inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') |
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generate_kwargs = dict( |
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inputs, |
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max_new_tokens=1024, |
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top_p=0.95, |
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top_k=50, |
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temperature=0.9, |
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do_sample=True, |
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num_beams=1, |
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) |
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r = model.generate(**generate_kwargs) |
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print(tokenizer.decode(r[0])) |
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``` |
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```text |
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<s> [INST] <<SYS>> |
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awak adalah AI yang mampu jawab segala soalan |
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<</SYS>> |
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awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun [/INST] Jabatan ini bertanggungjawab untuk mengeluarkan dan mengagihkan produk koko ke pasaran tempatan dan antarabangsa. Mereka juga menyumbang kepada pembangunan industri koko dan memastikan penggunaan sumber asli yang bertanggungjawab. Selain itu, mereka menjalankan penyelidikan dan inovasi untuk meningkatkan proses pengeluaran dan meningkatkan daya saing produk koko. </s> |
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``` |
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```python |
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f2 = { |
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'name': 'parse_entities', |
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'description': 'extract entities from the text', |
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'parameters': { |
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'type': 'object', |
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'properties': { |
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'drink': { |
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'type': 'string', |
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'description': 'drink name', |
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}, |
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'event': { |
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'type': 'string', |
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'description': 'event name', |
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}, |
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'person_name': { |
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'type': 'string', |
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'description': 'person name', |
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} |
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}, |
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'required': [ |
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'drink', |
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'event', |
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'person_name' |
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] |
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} |
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} |
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messages = [ |
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{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'}, |
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{'role': 'user', 'content': 'nama saya husein bin zolkepli, saya sekarang berada di putrajaya merdeka 2023 sambil minum teh o ais'} |
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] |
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prompt = parse_llama_chat(messages, function_call = [f2]) |
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inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda') |
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generate_kwargs = dict( |
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inputs, |
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max_new_tokens=128, |
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top_p=0.95, |
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top_k=50, |
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temperature=0.9, |
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do_sample=True, |
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num_beams=1, |
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) |
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r = model.generate(**generate_kwargs) |
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print(tokenizer.decode(r[0])) |
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``` |
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```text |
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<s> [INST] <<SYS>> |
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awak adalah AI yang mampu jawab segala soalan |
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<</SYS>> |
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[FUNCTIONCALL] |
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{ |
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"name": "parse_entities", |
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"description": "extract entities from the text", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"drink": { |
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"type": "string", |
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"description": "drink name" |
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}, |
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"event": { |
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"type": "string", |
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"description": "event name" |
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}, |
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"person_name": { |
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"type": "string", |
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"description": "person name" |
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} |
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}, |
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"required": [ |
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"drink", |
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"event", |
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"person_name" |
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] |
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} |
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} |
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nama saya husein bin zolkepli, saya sekarang berada di putrajaya merdeka 2023 sambil minum teh o ais [/INST] <functioncall> {"name": "parse_entities", "arguments": '{"drink": "teh o ais", "event": "Merdeka 2023", "person_name": "Husein bin Zolkepli"}'} |
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<functioncall> {"entities": [{"name": "Husein bin Zolkepli", "confidence": 0.95}]} </s> |
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``` |
