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Model: arshadshk/Mistral-Hinglish-7B-Instruct-v0.2
Source: Original Platform

README.md

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+---
+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](https://huggingface.co/datasets/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:
+| Model                 | Loss |
+|-----------------------|------|
+| Mistral-Hinglish-Instruct | 0.8  |
+| Mistral-Instruct       | 1.8  |
+
+## 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](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
+
+```python
+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])
+```
+
+