Update README.md
Browse files
README.md
CHANGED
|
@@ -6,14 +6,26 @@ license: llama2
|
|
| 6 |
|
| 7 |
<!-- Provide a quick summary of what the model is/does. -->
|
| 8 |
|
| 9 |
-
**dragon-llama-qa-tool** is a
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 10 |
|
| 11 |
Load in your favorite GGUF inference engine, or try with llmware as follows:
|
| 12 |
|
| 13 |
from llmware.models import ModelCatalog
|
| 14 |
|
| 15 |
-
|
| 16 |
-
|
|
|
|
|
|
|
|
|
|
| 17 |
|
| 18 |
|
| 19 |
### Model Description
|
|
@@ -23,17 +35,9 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
|
|
| 23 |
- **Developed by:** llmware
|
| 24 |
- **Model type:** GGUF
|
| 25 |
- **Language(s) (NLP):** English
|
| 26 |
-
- **License:**
|
| 27 |
-
- **Quantized from model:** llmware/dragon-llama
|
| 28 |
-
|
| 29 |
-
## Uses
|
| 30 |
-
|
| 31 |
-
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
| 32 |
-
|
| 33 |
-
All of the DRAGON models use the following prompt wrapper template:
|
| 34 |
-
|
| 35 |
-
"<human> " + context + "\n" + question + "\n<bot>: "
|
| 36 |
-
|
| 37 |
|
| 38 |
## Model Card Contact
|
| 39 |
|
|
|
|
| 6 |
|
| 7 |
<!-- Provide a quick summary of what the model is/does. -->
|
| 8 |
|
| 9 |
+
**dragon-llama-qa-tool** is a quantized version of DRAGON Llama 7B, with 4_K_M GGUF quantization, providing a fast, small inference implementation for use on CPUs.
|
| 10 |
+
|
| 11 |
+
[DRAGON LLama 7B](https://huggingface.co/llmware/dragon-llama-7b-v0) is a fact-based question-answering model, optimized for complex business documents.
|
| 12 |
+
|
| 13 |
+
We are providing as a separate repository that can be pulled directly:
|
| 14 |
+
|
| 15 |
+
from huggingface_hub import snapshot_download
|
| 16 |
+
|
| 17 |
+
snapshot_download("llmware/dragon-llama-answer-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
|
| 18 |
+
|
| 19 |
|
| 20 |
Load in your favorite GGUF inference engine, or try with llmware as follows:
|
| 21 |
|
| 22 |
from llmware.models import ModelCatalog
|
| 23 |
|
| 24 |
+
model = ModelCatalog().load_model("llmware/dragon-llama-qa-tool")
|
| 25 |
+
|
| 26 |
+
response = model.inference(query, text_sample)
|
| 27 |
+
|
| 28 |
+
Note: please review the config.json file in the repository for prompt wrapping information, details on the model, and full test set.
|
| 29 |
|
| 30 |
|
| 31 |
### Model Description
|
|
|
|
| 35 |
- **Developed by:** llmware
|
| 36 |
- **Model type:** GGUF
|
| 37 |
- **Language(s) (NLP):** English
|
| 38 |
+
- **License:** Llama 2 Community License
|
| 39 |
+
- **Quantized from model:** [llmware/dragon-llama](https://huggingface.co/llmware/dragon-llama-7b-v0/)
|
| 40 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 41 |
|
| 42 |
## Model Card Contact
|
| 43 |
|