arshadshk_-_Mistral-Hinglish-7B-Instruct-v0.2-gguf

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Mistral-Hinglish-7B-Instruct-v0.2 - GGUF

• Model creator: https://huggingface.co/arshadshk/
• Original model: https://huggingface.co/arshadshk/Mistral-Hinglish-7B-Instruct-v0.2/

Original model description:

license: apache-2.0 pipeline_tag: text-generation tags:

• finetuned
• lora inference: true widget:
• messages:
  • role: user content: What is your favorite condiment?

---

Training Details

30k chat sessions with a total of <= 1024 tokens were selected from the sarvamai/samvaad-hi-v1 dataset, with 2k sessions reserved for the test set. The Lora adapter is utilized and fine-tuned using SFT TRL. Test set loss:

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("arshadshk/Mistral-Hinglish-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("arshadshk/Mistral-Hinglish-7B-Instruct-v0.2")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])