Downloads · 30 days
65
14% of all-time downloads
RichardErkhov/abacusai_-_Dracarys-72B-Instruct-gguf
abacusai_-_Dracarys-72B-Instruct-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
65
14% of all-time downloads
All-time downloads
475
Public
Repo size
694 GB
Likes
0
Public
Click a slice to open those files.
.gguf780 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Dracarys-72B-Instruct - GGUF
| Name | Quant method | Size |
|---|---|---|
| Dracarys-72B-Instruct.Q2_K.gguf | Q2_K | 27.76GB |
| Dracarys-72B-Instruct.Q3_K_S.gguf | Q3_K_S | 32.12GB |
| Dracarys-72B-Instruct.Q3_K.gguf | Q3_K | 35.11GB |
| Dracarys-72B-Instruct.Q3_K_M.gguf | Q3_K_M | 35.11GB |
| Dracarys-72B-Instruct.Q3_K_L.gguf | Q3_K_L | 36.79GB |
| Dracarys-72B-Instruct.IQ4_XS.gguf | IQ4_XS | 37.4GB |
| Dracarys-72B-Instruct.Q4_0.gguf | Q4_0 | 38.4GB |
| Dracarys-72B-Instruct.IQ4_NL.gguf | IQ4_NL | 38.9GB |
| Dracarys-72B-Instruct.Q4_K_S.gguf | Q4_K_S | 40.88GB |
| Dracarys-72B-Instruct.Q4_K.gguf | Q4_K | 44.16GB |
| Dracarys-72B-Instruct.Q4_K_M.gguf | Q4_K_M | 44.16GB |
| Dracarys-72B-Instruct.Q4_1.gguf | Q4_1 | 42.56GB |
| Dracarys-72B-Instruct.Q5_0.gguf | Q5_0 | 46.72GB |
| Dracarys-72B-Instruct.Q5_K_S.gguf | Q5_K_S | 47.85GB |
| Dracarys-72B-Instruct.Q5_K.gguf | Q5_K | 50.71GB |
| Dracarys-72B-Instruct.Q5_K_M.gguf | Q5_K_M | 45.3GB |
| Dracarys-72B-Instruct.Q5_1.gguf | Q5_1 | 50.88GB |
| Dracarys-72B-Instruct.Q6_K.gguf | Q6_K | 59.93GB |
| Dracarys-72B-Instruct.Q8_0.gguf | Q8_0 | 16.64GB |
language:
We introduce the latest in the Smaug series, the Dracarys family of finetunes targeting coding performance improvements across a variety of base models.
This variant is a finetune of Qwen2-72B-Instruct
Compared to Qwen2-72B-Instruct, Dracarys has better LiveCodeBench scores (see evaluation results below).
The prompt format is unchanged from Qwen2-72B-Instruct (see evaluations for prompt details for LCB)
See the snippet below for usage with Transformers:
import transformers
import torch
model_id = "abacusai/Dracarys-72B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are data science coding assistant that generates Python code using Pandas and Numpy."},
{"role": "user", "content": "Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
| Model | Code Generation | Code Execution | Test Output Prediction |
|---|---|---|---|
| Dracarys-72B-Instruct | 33.57 | 62.96 | 58.93 |
| Qwen2-72B-Instruct | 32.92 | 58.95 | 55.88 |
| Model | Easy | Medium | Hard |
|---|---|---|---|
| Dracarys-72B-Instruct | 64.16 | 25.06 | 3.64 |
| Qwen2-72B-Instruct | 65.83 | 22.28 | 3.11 |
| Model | Easy | Medium | Hard |
|---|---|---|---|
| Dracarys-72B-Instruct | 65.37 | 58.74 | 46.38 |
| Qwen2-72B-Instruct | 63.19 | 54.08 | 46.52 |
| Model | Global Average | Coding Average | Language Average | Mathematics Average | Data Analysis Average | Reasoning Average | IF Average |
|---|---|---|---|---|---|---|---|
| Dracarys-72B-Instruct | 41.20 | 38.95 | 31.17 | 42.77 | 26.24 | 40 | 68.08 |
| Qwen2-72B-Instruct | 40.15 | 32.38 | 29.21 | 43.44 | 26.24 | 41.33 | 68.27 |