File size: 1,937 Bytes
c3c1eca e7a519f c3c1eca 52e05e7 c3c1eca 52e05e7 c3c1eca 52e05e7 c3c1eca 52e05e7 c3c1eca 52e05e7 c3c1eca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
license: apache-2.0
tags:
- finetuned
pipeline_tag: text-generation
inference: true
widget:
- messages:
- role: user
content: What is your favorite condiment?
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# Model Card for shaheerzk/text-to-rdb-queries
## Inference with hugging face `transformers`
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("shaheerzk/text-to-rdb-queries")
model.to("cuda")
generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True)
# decode with mistral tokenizer
result = tokenizer.decode(generated_ids[0].tolist())
print(result)
```
> [!TIP]
> PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome!
---
The shaheerzk/text-to-rdb-queries Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
## Instruction format
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("shaheerzk/text-to-rdb-queries")
tokenizer = AutoTokenizer.from_pretrained("shaheerzk/text-to-rdb-queries")
messages = [
{"role": "user", "content": ""},
{"role": "assistant", "content": ""},
{"role": "user", "content": ""}
]
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])
```
|