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- ggml-model-q4_k_m.gguf +3 -0
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README.md
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---
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language:
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- ko
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library_name: transformers
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pipeline_tag: text-generation
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license: cc-by-nc-4.0
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---
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# **Synatra-7B-v0.3-RPπ§**
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![Synatra-7B-v0.3-RP](./Synatra.png)
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## Support Me
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μλνΈλΌλ κ°μΈ νλ‘μ νΈλ‘, 1μΈμ μμμΌλ‘ κ°λ°λκ³ μμ΅λλ€. λͺ¨λΈμ΄ λ§μμ λμ
¨λ€λ©΄ μ½κ°μ μ°κ΅¬λΉ μ§μμ μ΄λ¨κΉμ?
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[<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy me a Coffee" width="217" height="50">](https://www.buymeacoffee.com/mwell)
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Wanna be a sponser? Contact me on Telegram **AlzarTakkarsen**
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# **License**
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This model is strictly [*non-commercial*](https://creativecommons.org/licenses/by-nc/4.0/) (**cc-by-nc-4.0**) use only.
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The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included **cc-by-nc-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
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The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.
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# **Model Details**
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**Base Model**
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[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
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**Trained On**
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A6000 48GB * 8
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**Instruction format**
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It follows [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) format.
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**TODO**
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- ~~``RP κΈ°λ° νλ λͺ¨λΈ μ μ``~~ β
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- ~~``λ°μ΄ν°μ
μ μ ``~~ β
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- μΈμ΄ μ΄ν΄λ₯λ ₯ κ°μ
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- ~~``μμ 보μ``~~ β
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- ν ν¬λμ΄μ λ³κ²½
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# **Model Benchmark**
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## Ko-LLM-Leaderboard
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On Benchmarking...
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# **Implementation Code**
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Since, chat_template already contains insturction format above.
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You can use the code below.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-7B-v0.3-RP")
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tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-RP")
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messages = [
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{"role": "user", "content": "λ°λλλ μλ νμμμ΄μΌ?"},
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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# Why It's benchmark score is lower than preview version?
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**Apparently**, Preview model uses Alpaca Style prompt which has no pre-fix. But ChatML do.
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ggml-model-q4_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:59a29596cbe27a2a0e646d82cb559d63e8e08b3f6e303b00d442613028fb4110
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size 4368450240
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